In this notebook we’ll be looking at the gene-level count data from the RNA-seq. We’ll perform DGE tests for each bioproject separately, look at combining DGE related results using p-value metaanalysis, and look at normalising counts for downstream co-expression analysis.
The first think is to load the data and see what we’re dealing with.
library(tidyverse)
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library(ruv)
library(DESeq2) # bioconductor
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library(vsn) # bioconductor
library(edgeR) # bioconductor
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library(ggplot2)
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library(sva)
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library(ComplexHeatmap) # bioconductor
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ggplot2::theme_set(ggplot2::theme_bw() +
ggplot2::theme(
strip.background = element_rect(
color="black", fill="white", linewidth=0, linetype="solid"
),
rect = element_rect(
colour = "black",
linewidth = 1,
linetype = "solid"
)
)
)
okabe <- c('#000000', '#E69F00', '#56B4E9', '#009E73', '#F0E042', '#0072B2', '#D55E00', '#CC79A7', '#999999')
options(ggplot2.discrete.fill = list(okabe[seq(2, 9, 2)], okabe[2:8], okabe, rep(okabe, 10)))
options(ggplot2.discrete.colour = list(okabe[seq(2, 9, 2)], okabe[2:8], okabe, rep(okabe, 10)))
gg_shape <- function(gg, vals) {gg + scale_shape_manual(values = rep(c(15, 17:20), 100))}
counts_raw <- readr::read_delim("output/feature_counts.tsv", delim = "\t") %>%
mutate(Geneid = str_remove(Geneid, "gene-"))
## Rows: 11130 Columns: 431
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (5): Geneid, Chr, Start, End, Strand
## dbl (426): Length, SRR10189427, SRR10189429, SRR10189431, SRR10189435, SRR10...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(counts_raw)
## # A tibble: 6 × 431
## Geneid Chr Start End Strand Length SRR10189427 SRR10189429 SRR10189431
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 sscle_01g… chr01 416 1399 - 984 635 666 561
## 2 sscle_01g… chr0… 1997… 2294… +;+;+ 912 557 551 670
## 3 sscle_01g… chr0… 3349… 3902… -;- 729 225 189 226
## 4 sscle_01g… chr0… 4747… 5128… +;+ 1410 762 841 856
## 5 sscle_01g… chr0… 7185… 7735… -;-;- 2091 351 705 567
## 6 sscle_01g… chr01 13600 15537 + 1938 599 654 767
## # ℹ 422 more variables: SRR10189435 <dbl>, SRR10189436 <dbl>,
## # SRR10189437 <dbl>, SRR10189441 <dbl>, SRR10189442 <dbl>, SRR10189443 <dbl>,
## # SRR10189447 <dbl>, SRR10189448 <dbl>, SRR10189449 <dbl>, SRR10277943 <dbl>,
## # SRR10277944 <dbl>, SRR10277945 <dbl>, SRR10277946 <dbl>, SRR10277947 <dbl>,
## # SRR10277948 <dbl>, SRR10277949 <dbl>, SRR10277950 <dbl>, SRR10277951 <dbl>,
## # SRR10277952 <dbl>, SRR10277953 <dbl>, SRR10277954 <dbl>, SRR10277955 <dbl>,
## # SRR10277956 <dbl>, SRR10277957 <dbl>, SRR10277958 <dbl>, …
counts <- counts_raw %>%
mutate(id=Geneid) %>%
select(-Chr, -Start, -End, -Strand, -Length, -Geneid)
# There must be a more elegant way to do this...
counts <- counts[, c("id", colnames(counts)[colnames(counts) != "id"])]
head(counts)
## # A tibble: 6 × 426
## id SRR10189427 SRR10189429 SRR10189431 SRR10189435 SRR10189436 SRR10189437
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 sscle… 635 666 561 426 263 373
## 2 sscle… 557 551 670 478 362 454
## 3 sscle… 225 189 226 134 140 181
## 4 sscle… 762 841 856 487 458 524
## 5 sscle… 351 705 567 5183 7189 9574
## 6 sscle… 599 654 767 872 1501 901
## # ℹ 419 more variables: SRR10189441 <dbl>, SRR10189442 <dbl>,
## # SRR10189443 <dbl>, SRR10189447 <dbl>, SRR10189448 <dbl>, SRR10189449 <dbl>,
## # SRR10277943 <dbl>, SRR10277944 <dbl>, SRR10277945 <dbl>, SRR10277946 <dbl>,
## # SRR10277947 <dbl>, SRR10277948 <dbl>, SRR10277949 <dbl>, SRR10277950 <dbl>,
## # SRR10277951 <dbl>, SRR10277952 <dbl>, SRR10277953 <dbl>, SRR10277954 <dbl>,
## # SRR10277955 <dbl>, SRR10277956 <dbl>, SRR10277957 <dbl>, SRR10277958 <dbl>,
## # SRR10277959 <dbl>, SRR10277960 <dbl>, SRR10590602 <dbl>, …
meta <- readr::read_delim("input/sra_rnaseq2.tsv", delim = "\t", na = "-") %>%
mutate(
host_ = paste0(host_tidyname, host_accession_tidyname),
organism_ = paste0(organism_tidyname, accession_tidyname),
) %>%
filter(organism != "Sclerotinia trifoliorum")
## Rows: 621 Columns: 30
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (23): sra, bioproject, biosample, DOI, platform, strategy, stranded, inc...
## dbl (7): read_length, n_fragments, n_fragments_filtered, n_fragments_aligne...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(meta)
## # A tibble: 6 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR1582088 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 2 SRR1582089 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 3 SRR1582090 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 4 SRR1582091 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 5 SRR1582133 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 6 SRR1582134 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
OK. The data is in, let’s look at the counts.
ggplot(meta, aes(x=sra, y=n_fragments_aligned, fill=included)) +
geom_bar(stat="identity") +
facet_wrap(vars(bioproject), scales="free")
## Warning: Removed 5 rows containing missing values (`position_stack()`).
So you should see that there are several bioprojects that I’ve excluded on the basis of multimapping reads, generally low read coverage, etc. The samples labelled “network only” are excluded from DGE because they are unreplicated. However they may be of interest to identify co-expressed genes.
You should also see that we have a pretty wide range of coverage. Typically the most dramatic differences are between samples in planta and in vitro.
Sometimes a biological treatment will be split across multiple sequencing runs. I’ll check for this now.
multi_biosample <- meta %>% group_by(biosample) %>% summarise(n = n_distinct(sra)) %>% filter(n > 1) %>% .[["biosample"]]
for (m in multi_biosample) {
print(meta[meta$biosample == m, ])
}
## # A tibble: 6 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR1582088 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 2 SRR1582089 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 3 SRR1582090 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 4 SRR1582091 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 5 SRR1582133 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 6 SRR1582134 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 6 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR1582140 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 2 SRR1582148 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 3 SRR1582200 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 4 SRR1582201 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 5 SRR1582461 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 6 SRR1582462 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 6 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR1582469 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 2 SRR1582470 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 3 SRR1582471 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 4 SRR1582472 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 5 SRR1582473 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 6 SRR1582474 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 2 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR8306347 PRJNA379694 SAMN06618… http… ILLUMINA 100 SE R
## 2 SRR8306348 PRJNA379694 SAMN06618… http… ILLUMINA 100 SE R
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 2 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR8305679 PRJNA379694 SAMN10587… http… ILLUMINA 100 SE R
## 2 SRR8305680 PRJNA379694 SAMN10587… http… ILLUMINA 100 SE R
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 12 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR9200308 PRJNA546228 SAMN1193… http… ILLUMINA 150 PE unstran…
## 2 SRR9200309 PRJNA546228 SAMN1193… http… ILLUMINA 150 PE unstran…
## 3 SRR9200310 PRJNA546228 SAMN1193… http… ILLUMINA 150 PE unstran…
## 4 SRR9200311 PRJNA546228 SAMN1193… http… ILLUMINA 150 PE unstran…
## 5 SRR9200312 PRJNA546228 SAMN1193… http… ILLUMINA 150 PE unstran…
## 6 SRR9200313 PRJNA546228 SAMN1193… http… ILLUMINA 150 PE unstran…
## 7 SRR9200314 PRJNA546228 SAMN1193… http… ILLUMINA 150 PE unstran…
## 8 SRR9200315 PRJNA546228 SAMN1193… http… ILLUMINA 150 PE unstran…
## 9 SRR9200316 PRJNA546228 SAMN1193… http… ILLUMINA 150 PE unstran…
## 10 SRR9200317 PRJNA546228 SAMN1193… http… ILLUMINA 150 PE unstran…
## 11 SRR9200318 PRJNA546228 SAMN1193… http… ILLUMINA 150 PE unstran…
## 12 SRR9200319 PRJNA546228 SAMN1193… http… ILLUMINA 150 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 12 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR10590602 PRJNA5937… SAMN1348… http… ILLUMINA 150 PE unstran…
## 2 SRR10590603 PRJNA5937… SAMN1348… http… ILLUMINA 150 PE unstran…
## 3 SRR10590604 PRJNA5937… SAMN1348… http… ILLUMINA 150 PE unstran…
## 4 SRR10590605 PRJNA5937… SAMN1348… http… ILLUMINA 150 PE unstran…
## 5 SRR10590606 PRJNA5937… SAMN1348… http… ILLUMINA 150 PE unstran…
## 6 SRR10590607 PRJNA5937… SAMN1348… http… ILLUMINA 150 PE unstran…
## 7 SRR10590608 PRJNA5937… SAMN1348… http… ILLUMINA 150 PE unstran…
## 8 SRR10590609 PRJNA5937… SAMN1348… http… ILLUMINA 150 PE unstran…
## 9 SRR10590610 PRJNA5937… SAMN1348… http… ILLUMINA 150 PE unstran…
## 10 SRR10590611 PRJNA5937… SAMN1348… http… ILLUMINA 150 PE unstran…
## 11 SRR10590612 PRJNA5937… SAMN1348… http… ILLUMINA 150 PE unstran…
## 12 SRR10590613 PRJNA5937… SAMN1348… http… ILLUMINA 150 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 6 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR12072338 PRJNA641217 SAMN1534… <NA> ILLUMINA 150 PE FR
## 2 SRR12072339 PRJNA641217 SAMN1534… <NA> ILLUMINA 150 PE FR
## 3 SRR12072340 PRJNA641217 SAMN1534… <NA> ILLUMINA 150 PE FR
## 4 SRR12072341 PRJNA641217 SAMN1534… <NA> ILLUMINA 150 PE FR
## 5 SRR12072344 PRJNA641217 SAMN1534… <NA> ILLUMINA 150 PE FR
## 6 SRR12072345 PRJNA641217 SAMN1534… <NA> ILLUMINA 150 PE FR
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 6 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR12072334 PRJNA641217 SAMN1534… <NA> ILLUMINA 150 PE FR
## 2 SRR12072335 PRJNA641217 SAMN1534… <NA> ILLUMINA 150 PE FR
## 3 SRR12072336 PRJNA641217 SAMN1534… <NA> ILLUMINA 150 PE FR
## 4 SRR12072337 PRJNA641217 SAMN1534… <NA> ILLUMINA 150 PE FR
## 5 SRR12072342 PRJNA641217 SAMN1534… <NA> ILLUMINA 150 PE FR
## 6 SRR12072343 PRJNA641217 SAMN1534… <NA> ILLUMINA 150 PE FR
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 6 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR12077446 PRJNA641462 SAMN1535… <NA> ILLUMINA 150 PE FR
## 2 SRR12077447 PRJNA641462 SAMN1535… <NA> ILLUMINA 150 PE FR
## 3 SRR12077448 PRJNA641462 SAMN1535… <NA> ILLUMINA 150 PE FR
## 4 SRR12077449 PRJNA641462 SAMN1535… <NA> ILLUMINA 150 PE FR
## 5 SRR12077450 PRJNA641462 SAMN1535… <NA> ILLUMINA 150 PE FR
## 6 SRR12077451 PRJNA641462 SAMN1535… <NA> ILLUMINA 150 PE FR
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 3 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR13570946 PRJNA695466 SAMN1761… http… ILLUMINA 150 PE FR
## 2 SRR13570947 PRJNA695466 SAMN1761… http… ILLUMINA 150 PE FR
## 3 SRR13570948 PRJNA695466 SAMN1761… http… ILLUMINA 150 PE FR
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 3 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR13570943 PRJNA695466 SAMN1761… http… ILLUMINA 150 PE FR
## 2 SRR13570944 PRJNA695466 SAMN1761… http… ILLUMINA 150 PE FR
## 3 SRR13570945 PRJNA695466 SAMN1761… http… ILLUMINA 150 PE FR
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 6 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR15058739 PRJNA744325 SAMN2008… http… ILLUMINA 150 PE unstran…
## 2 SRR15058740 PRJNA744325 SAMN2008… http… ILLUMINA 150 PE unstran…
## 3 SRR15058741 PRJNA744325 SAMN2008… http… ILLUMINA 150 PE unstran…
## 4 SRR15058742 PRJNA744325 SAMN2008… http… ILLUMINA 150 PE unstran…
## 5 SRR15058743 PRJNA744325 SAMN2008… http… ILLUMINA 150 PE unstran…
## 6 SRR15058744 PRJNA744325 SAMN2008… http… ILLUMINA 150 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 2 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR22227239 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## 2 SRR22227240 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 2 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR22227241 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## 2 SRR22227242 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 2 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR22227243 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## 2 SRR22227244 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 2 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR22227245 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## 2 SRR22227246 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 2 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR22227247 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## 2 SRR22227248 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 2 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR22227249 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## 2 SRR22227250 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 2 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR22227257 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## 2 SRR22227258 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 2 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR22227259 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## 2 SRR22227260 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 2 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR22227261 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## 2 SRR22227262 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 2 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR22227263 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## 2 SRR22227264 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
## # A tibble: 2 × 32
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR22227265 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## 2 SRR22227266 PRJNA899438 SAMN3165… <NA> ILLUMINA 100 PE unstran…
## # ℹ 24 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
Ok. So these all seem to be treatment replicates. I’m not going to worry about combining any counts.
We’ll exclude any samples that we aren’t going to keep right now. And I want to convert the table to a long format which i can use to select subsets very easily.
meta <- meta[meta$included %in% c("TRUE", "NETWORK_ONLY"), ]
lcounts <- dplyr::inner_join(
meta,
tidyr::pivot_longer(counts, -id, names_to = "sra", values_to = "count"),
by = "sra"
) %>% mutate(cpm = count / (n_fragments_aligned / 1000000)) %>%
select(-platform, -read_length, -strategy, -stranded, -n_fragments, -n_fragments_filtered, -n_fragments_aligned, -n_fragments_aligned_single, -n_fragments_aligned_multi, -notes, -DOI, -biosample)
head(lcounts)
## # A tibble: 6 × 23
## sra bioproject included sample organism organism_tidyname accession
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 SRR1582088 PRJNA261444 TRUE PRJNA261… Sclerot… Sscl Scl02-05
## 2 SRR1582088 PRJNA261444 TRUE PRJNA261… Sclerot… Sscl Scl02-05
## 3 SRR1582088 PRJNA261444 TRUE PRJNA261… Sclerot… Sscl Scl02-05
## 4 SRR1582088 PRJNA261444 TRUE PRJNA261… Sclerot… Sscl Scl02-05
## 5 SRR1582088 PRJNA261444 TRUE PRJNA261… Sclerot… Sscl Scl02-05
## 6 SRR1582088 PRJNA261444 TRUE PRJNA261… Sclerot… Sscl Scl02-05
## # ℹ 16 more variables: accession_tidyname <chr>, host <chr>,
## # host_tidyname <chr>, host_accession <chr>, host_accession_tidyname <chr>,
## # tissue <chr>, hpi <dbl>, plant_tissue <chr>, treatment <chr>,
## # treatment_tidy <chr>, sample_material <chr>, host_ <chr>, organism_ <chr>,
## # id <chr>, count <dbl>, cpm <dbl>
Let’s have a look at some PCA plots.
tmp <- lcounts %>% tidyr::pivot_wider(id_cols = c(id), names_from = sra, values_from = cpm) %>%
column_to_rownames("id") %>%
as.matrix()
pca_raw <- princomp(tmp, fix_sign = TRUE)
prop_var <- ((pca_raw$sdev[1:10] / sum(pca_raw$sdev[1:10])) * 100) %>%
data.frame(variance_explained = .) %>%
rownames_to_column("PC") %>%
mutate(PC = paste0("PC", sprintf("%02d", 1:n())))
ggplot(prop_var, aes(x=PC, y=variance_explained)) + geom_bar(stat = "identity")
The first PC explains a fairly large amount of variance. I wasn’t expecting to get much reduction in this dataset.
pca <- as.data.frame(pca_raw$loadings[, 1:10])
colnames(pca) <- paste0("PC", sprintf("%02d", seq_len(ncol(pca))))
pca <- pca %>% rownames_to_column("sra")
pca <- right_join(meta, pca, by = "sra")
head(pca)
## # A tibble: 6 × 42
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR1582088 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 2 SRR1582089 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 3 SRR1582090 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 4 SRR1582091 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 5 SRR1582133 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 6 SRR1582134 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## # ℹ 34 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
gg <- ggplot(pca, aes(x=PC01, y=PC02, color=host_tidyname, shape=host_tidyname, fill=host_tidyname)) +
geom_point(alpha = 0.7, size = 3)
gg <- gg_shape(gg, pca$host_tidyname)
print(gg)
gg <- ggplot(pca, aes(x=PC03, y=PC04, color=host_tidyname, fill=host_tidyname, shape=host_tidyname)) +
geom_point(alpha = 0.7, size = 3)
gg <- gg_shape(gg, pca$host_tidyname)
gg
Well, the first and second axes seem to be picking something up with B. napus samples, and maybe some unusual in-vitro samples. It looks like the B. napus samples are going to be dominating the data. Possibly just because there’s more of it.
gg <- ggplot(pca, aes(x=PC01, y=PC02, color=bioproject, fill=bioproject, shape=bioproject)) +
geom_point(alpha = 0.7, size = 3)
gg <- gg_shape(gg, pca$bioproject)
print(gg)
gg <- ggplot(pca, aes(x=PC03, y=PC04, color=bioproject, fill=bioproject, shape=bioproject)) +
geom_point(alpha = 0.7, size = 3)
gg <- gg_shape(gg, pca$bioproject)
print(gg)
It does seem that the little clusters are at least coming from the same bioprojects, which is encouraging. Maybe a heatmap would be nice to show how the different experiments are (dis)similar.
# Utility function from https://jokergoo.github.io/2020/05/11/set-cell-width/height-in-the-heatmap/
calc_ht_size = function(ht, unit = "inches") {
pdf(NULL)
ht = draw(ht)
w = ComplexHeatmap:::width(ht)
w = convertX(w, unit, valueOnly = TRUE)
h = ComplexHeatmap:::height(ht)
h = convertY(h, unit, valueOnly = TRUE)
dev.off()
c(w, h)
}
We’ll look at clustering the samples to see if natural groups fall out from a correlation matrix.
tmp <- lcounts %>% tidyr::pivot_wider(id_cols = c(id), names_from = sra, values_from = cpm) %>%
column_to_rownames("id") %>%
as.matrix()
tmp <- log(tmp + 1, base = 2)
tmp_cor <- cor(tmp)
tmp_dist <- as.dist(1 - tmp_cor)
hcl <- hclust(tmp_dist, method = "average")
srrs <- colnames(tmp)[hcl$order]
ha <- HeatmapAnnotation(
bioproject = meta %>% column_to_rownames("sra") %>% .[srrs, "bioproject"],
host = meta %>% column_to_rownames("sra") %>% .[srrs, "host"]
)
ht <- Heatmap(
tmp_cor,
name = "samples",
cluster_rows = hcl,
cluster_columns = hcl,
show_column_dend = FALSE,
show_column_names = FALSE,
show_row_names = FALSE,
top_annotation = ha,
height=nrow(tmp_cor) * unit(0.5, "mm"),
width=ncol(tmp_cor) * unit(0.5, "mm")
)
htsize <- calc_ht_size(ht)
htsize
## [1] 12.822730 8.906201
pdf("output/06-samples_correlation_clustered.pdf", width = htsize[1], height = htsize[2])
ht
dev.off()
## png
## 2
draw(ht)
So you can see from the top-colour bar that the clusters are a mixture of bioprojects and hosts. Host tends to form larger blocks, but not all samples from the same host are positioned together.
Next we’ll look at an unclustered matrix, so samples from the same bioproject should occur together.
tmp <- lcounts %>% tidyr::pivot_wider(id_cols = c(id), names_from = sra, values_from = cpm) %>%
column_to_rownames("id") %>%
as.matrix()
tmp <- log(tmp + 1, base = 2)
tmp_cor <- cor(tmp)
#tmp_dist <- as.dist(1 - tmp_cor)
#hcl <- hclust(tmp_dist, method = "average")
#srrs <- colnames(tmp)[hcl$order]
ha <- HeatmapAnnotation(
bioproject = meta %>% column_to_rownames("sra") %>% .[colnames(tmp), "bioproject"],
host = meta %>% column_to_rownames("sra") %>% .[colnames(tmp), "host"]
)
ht <- Heatmap(
tmp_cor,
name = "samples",
cluster_rows = FALSE,
cluster_columns = FALSE,
show_column_dend = FALSE,
show_column_names = FALSE,
show_row_names = FALSE,
top_annotation = ha,
height=nrow(tmp_cor) * unit(0.5, "mm"),
width=ncol(tmp_cor) * unit(0.5, "mm")
)
htsize <- calc_ht_size(ht)
htsize
## [1] 12.409344 8.906201
pdf("output/06-samples_correlation_bioproject.pdf", width = htsize[1], height = htsize[2])
ht
dev.off()
## png
## 2
draw(ht)
And finally we’ll look at host.
tmp <- lcounts %>% tidyr::pivot_wider(id_cols = c(id), names_from = sra, values_from = cpm) %>%
column_to_rownames("id") %>%
as.matrix()
tmp <- log(tmp + 1, base = 2)
meta_tmp <- meta[order(meta$host, meta$host_accession, meta$hpi, meta$tissue, meta$accession, meta$plant_tissue, meta$treatment),]
tmp <- tmp[, meta_tmp$sra[meta_tmp$sra %in% colnames(tmp)]]
tmp_cor <- cor(tmp)
#tmp_dist <- as.dist(1 - tmp_cor)
#hcl <- hclust(tmp_dist, method = "average")
#srrs <- colnames(tmp)[hcl$order]
ha <- HeatmapAnnotation(
bioproject = meta %>% column_to_rownames("sra") %>% .[colnames(tmp), "bioproject"],
host = meta %>% column_to_rownames("sra") %>% .[colnames(tmp), "host"]
)
ht <- Heatmap(
tmp_cor,
name = "samples",
cluster_rows = FALSE,
cluster_columns = FALSE,
show_column_dend = FALSE,
show_column_names = FALSE,
show_row_names = FALSE,
top_annotation = ha,
height=nrow(tmp_cor) * unit(0.5, "mm"),
width=ncol(tmp_cor) * unit(0.5, "mm")
)
htsize <- calc_ht_size(ht)
htsize
## [1] 12.409344 8.906201
pdf("output/06-samples_correlation_host.pdf", width = htsize[1], height = htsize[2])
ht
dev.off()
## png
## 2
draw(ht)
There are a couple of good clusters there. It looks like the Lettuce (Lactuca sativa) samples are all well correlated. We’ll see i guess.
So i’m going to need to process each experiment separately. Which means some quick PCA, dispersion, and applying the contrasts. To save time i’ll try to automate as much as I can.
We’ll use DESeq2, which is my favourite as it handles genes with low counts better than EdgeR.
Here are the different bioprojects.
unique(meta$bioproject)
## [1] "PRJNA261444" "PRJNA327437" "PRJNA341340" "PRJNA418121" "PRJNA471709"
## [6] "PRJNA477716" "PRJNA501892" "PRJNA516496" "PRJNA574280" "PRJNA577619"
## [11] "PRJNA593737" "PRJNA601001" "PRJNA603456" "PRJNA607858" "PRJNA641217"
## [16] "PRJNA641462" "PRJNA643804" "PRJNA647983" "PRJNA670487" "PRJNA687280"
## [21] "PRJNA695466" "PRJNA706136" "PRJNA735329" "PRJNA744751" "PRJNA766564"
## [26] "PRJNA777355" "PRJNA789389" "PRJNA804213" "PRJNA830457" "PRJNA874732"
plotPCA2 <- function(dds, f1, f2) {
pcaData <- plotPCA(
dds,
ntop = 1000,
intgroup = c(f1, f2),
returnData = TRUE
)
percentVar <- round(100 * attr(pcaData, "percentVar"))
gg <- ggplot(pcaData, aes(x = PC1, y = PC2, color = .data[[f1]], shape = .data[[f2]])) +
geom_point(size=3, alpha=0.75) +
xlab(paste0("PC1: ", percentVar[1], "% variance")) +
ylab(paste0("PC2: ", percentVar[2], "% variance"))
gg <- gg_shape(gg, pcaData[[f2]])
return(gg)
}
# Converts 0 to the smallest possible number before log calc
get_log <- function(vec){
vec[vec == 0] <- .Machine$double.xmin
vec <- -log10(vec)
}
reverselog_trans <- function(base = exp(1)) {
trans <- function(x) -log(x, base)
inv <- function(x) base^(-x)
trans_new(paste0("reverselog-", format(base)), trans, inv,
log_breaks(base = base),
domain = c(1e-100, Inf))
}
plot_volcano <- function(df, title=NA, nlabels = 5, padj_threshold = 0.05) {
df <- df[!is.na(df$padj), ]
df_up <- df[(df$log2FoldChange > 0),]
df_up <- df_up[order(df_up$padj),]
df_up$label <- NA
df_up$label[1:nlabels] <- rownames(df_up)[1:nlabels]
df_down <- df[(df$log2FoldChange <= 0),]
df_down <- df_down[order(df_down$padj),]
df_down$label <- NA
df_down$label[1:nlabels] <- rownames(df_down)[1:nlabels]
df <- rbind(df_up, df_down)
gg <- df %>%
as.data.frame() %>%
ggplot(aes(y=padj, x=log2FoldChange, colour=padj < padj_threshold, label = label)) +
geom_text_repel(colour="black", box.padding=0.3, min.segment.length = 0, size=3) +
geom_point(alpha=0.5) +
scale_y_continuous(trans = reverselog_trans(10)) +
ylab("adjusted p-value") +
xlab(expression(paste(log[2] * " FC"))) +
labs(colour=paste0("adj. p < ", padj_threshold)) +
if ( !is.na(title) ) {
gg <- gg + ggtitle(title)
}
gg <- ggMarginal(gg, margins = "x", type="histogram", fill="lightgrey", xparams = list(bins = 100, linewidth = 0.25, fill = "black"), size=4)
return(gg)
}
process_contrast_mat <- function(bpr, contrasts, this_meta) {
colnames(contrasts) <- c("sample1", "sample2", "contrast_group")
contrasts <- as.data.frame(contrasts)
contrasts["contrast"] <- apply(contrasts, MARGIN = 1, FUN = function(x) {paste0(x["sample1"], "_vs_", x["sample2"])})
contrasts["bioproject"] <- bpr
contrasts <- dplyr::left_join(
contrasts,
this_meta %>% select(sample1 = sample, host1 = host_tidyname, hpi1 = hpi) %>% unique(),
by="sample1"
)
contrasts <- dplyr::left_join(
contrasts,
this_meta %>% select(sample2 = sample, host2 = host_tidyname, hpi2 = hpi) %>% unique(),
by="sample2"
)
contrasts <- contrasts[, c('contrast_group', 'bioproject', 'contrast', 'sample1', 'sample2', 'host1', 'hpi1', 'host2', 'hpi2')]
return(contrasts)
}
run_deg_tests <- function(lcounts, contrasts, bpr, norm_only = FALSE, exclude = NULL) {
this_counts <- lcounts %>% filter(bioproject == bpr) %>% tidyr::pivot_wider(id_cols = c(id), names_from = sra, values_from = count) %>%
column_to_rownames("id") %>%
as.matrix()
all(rownames(this_meta) == colnames(this_counts))
this_meta_ <- this_meta %>% column_to_rownames("sra") %>% mutate(sample = factor(sample)) %>% .[colnames(this_counts), ]
dds <- DESeqDataSetFromMatrix(
countData = this_counts,
colData = this_meta_,
design = ~ sample
)
ncounts <- assay(rlog(dds, blind = FALSE))
print(plotPCA(rlog(dds, blind = FALSE), intgroup = "sample"))
if (norm_only) {
return(list("norm_counts" = ncounts))
}
if (!is.null(exclude)) {
this_counts <- lcounts %>% filter(bioproject == bpr) %>% filter(!sra %in% exclude) %>% tidyr::pivot_wider(id_cols = c(id), names_from = sra, values_from = count) %>%
column_to_rownames("id") %>%
as.matrix()
all(rownames(this_meta) == colnames(this_counts))
this_meta_ <- this_meta %>% column_to_rownames("sra") %>% mutate(sample = factor(sample)) %>% .[colnames(this_counts), ]
dds <- DESeqDataSetFromMatrix(
countData = this_counts,
colData = this_meta_,
design = ~ sample
)
}
dds_results <- DESeq(dds, test = "Wald")
filter <- HTSFilter(dds_results, plot = TRUE, s.len=100)$filteredData
# Normally i prefer to run lfcThreshold=1, but the meta analysis methods require a uniform p-value
# distribution, and thresholded tests yield a big peak around pvalue=1.
tests <- apply(
contrasts,
MARGIN = 1,
FUN = function(x) {results(dds_results, contrast = c("sample", x["sample1"], x["sample2"]), alpha = 0.01, lfcThreshold = 0, independentFiltering=FALSE)}
)
names(tests) <- contrasts$contrast
gg <- do.call(
rbind,
lapply(names(tests), FUN=function(n) {data.frame(contrast=n, pvalue=tests[[n]]$pvalue)})
) %>%
ggplot(aes(x=pvalue)) +
geom_histogram() +
facet_wrap(vars(contrast))
print(gg)
mapper <- function(t) {
t2 <- as.data.frame(t) %>% summarise(
up = sum((log2FoldChange > 0) & (padj < 0.05), na.rm = TRUE),
down = sum((log2FoldChange < 0) & (padj < 0.05), na.rm = TRUE),
zerocounts = sum(baseMean == 0, na.rm = TRUE),
n = n()
)
return(t2)
}
# After HTSfilter these stats are no longer relevant
#lowcount_threshold = t@metadata$filterThreshold,
#lowcounts = sum(baseMean < t@metadata$filterThreshold, na.rm = TRUE),
print(do.call(rbind, lapply(tests, mapper)))
return(list("norm_counts" = ncounts, "tests" = tests))
}
run_count_norm <- function(lcounts, bpr) {
this_counts <- lcounts %>% filter(bioproject == bpr) %>% tidyr::pivot_wider(id_cols = c(id), names_from = sra, values_from = count) %>%
column_to_rownames("id") %>%
as.matrix()
all(rownames(this_meta) == colnames(this_counts))
this_meta_ <- this_meta %>% column_to_rownames("sra") %>% mutate(sample = factor(sample)) %>% .[colnames(this_counts), ]
dds <- DESeqDataSetFromMatrix(
countData = this_counts,
colData = this_meta_,
design = ~ 1
)
ncounts <- assay(rlog(dds, blind = TRUE))
print(plotPCA(rlog(dds, blind = FALSE), intgroup = "sample"))
return(list("norm_counts" = ncounts))
}
all_contrasts <- list()
norm_counts <- list()
deg_results <- list()
bpr <- "PRJNA261444"
this_meta <- meta %>% filter(bioproject == bpr)
# this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi", "PRJNA261444_SsclScl0205_periphery_IVPDA_stem_12hpi", "IPvsIV",
"PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi", "PRJNA261444_SsclScl0205_periphery_IVPDA_stem_24hpi", "IPvsIV",
"PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi", "PRJNA261444_SsclScl0205_periphery_IVPDA_stem_48hpi", "IPvsIV",
"PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi", "PRJNA261444_SsclScl0205_periphery_IVPDA_stem_12hpi", "IPvsIV",
"PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi", "PRJNA261444_SsclScl0205_periphery_IVPDA_stem_24hpi", "IPvsIV",
"PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi", "PRJNA261444_SsclScl0205_periphery_IVPDA_stem_48hpi", "IPvsIV",
"PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi", "PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi", "MvsE",
"PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi", "PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi", "LvsE",
"PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi", "PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi", "LvsE",
"PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi", "PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi", "MvsERes",
"PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi", "PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi", "LvsERes",
"PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi", "PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi", "LvsMRes",
"PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi", "PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi", "SuscvsRes",
"PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi", "PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi", "SuscvsRes",
"PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi", "PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi", "SuscvsRes"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 4455 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_12hpi 936
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_24hpi 1790
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_48hpi 2347
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_12hpi 773
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_24hpi 1550
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_48hpi 2317
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi 622
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi 1380
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi 521
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi 557
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi 1680
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi 851
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi 2
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi 75
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi 0
## down
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_12hpi 268
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_24hpi 1244
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_48hpi 2147
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_12hpi 223
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_24hpi 1230
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_48hpi 2214
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi 499
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi 1229
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi 329
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi 293
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi 1363
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi 574
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi 0
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi 14
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi 1
## zerocounts
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_12hpi 297
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_24hpi 297
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_48hpi 297
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_12hpi 297
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_24hpi 297
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_48hpi 297
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi 297
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi 297
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi 297
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi 297
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi 297
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi 297
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi 297
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi 297
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi 297
## n
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_12hpi 11130
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_24hpi 11130
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_48hpi 11130
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_12hpi 11130
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_24hpi 11130
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi_vs_PRJNA261444_SsclScl0205_periphery_IVPDA_stem_48hpi 11130
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi 11130
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi 11130
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi 11130
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi 11130
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi 11130
## PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi 11130
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_12hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_12hpi 11130
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_24hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_24hpi 11130
## PRJNA261444_SsclScl0205_lesion_PsatLifter_stem_48hpi_vs_PRJNA261444_SsclScl0205_lesion_PsatPI240515_stem_48hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA327437"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi", "PRJNA327437_Sscl1980_colony_IVMMGlu", "IPvsIV",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi", "PRJNA327437_Sscl1980_colony_IVMMGlu", "IPvsIV",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi", "PRJNA327437_Sscl1980_colony_IVMMGlu", "IPvsIV",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi", "PRJNA327437_Sscl1980_colony_IVMMGlu", "IPvsIV",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi", "PRJNA327437_Sscl1980_colony_IVMMGlu", "IPvsIV",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi", "PRJNA327437_Sscl1980_colony_IVMMGlu", "IPvsIV",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi", "PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi", "MvsE",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi", "PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi", "MvsE",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi", "PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi", "LvsE",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi", "PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi", "MvsE",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi", "PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi", "MvsE",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi", "PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi", "LvsE",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi", "PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi", "MvsE",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi", "PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi", "MvsE",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi", "PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi", "LvsE",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi", "PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi", "LvsM",
"PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi", "PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi", "LvsM"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 15623 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 1630
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 1556
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 1256
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 1071
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 1946
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 2168
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi 1423
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi 2322
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi 2445
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi 860
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi 1918
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi 2213
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi 302
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi 1666
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi 2171
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi 2063
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi 1575
## down
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 1392
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 1228
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 841
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 866
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 1362
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 1529
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi 1490
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi 2095
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi 2057
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi 1147
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi 1913
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi 2015
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi 625
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi 1609
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi 1892
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi 1680
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi 1451
## zerocounts
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi 919
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi 919
## n
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_colony_IVMMGlu 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_1hpi 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_3hpi 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_6hpi 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_12hpi 11130
## PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_48hpi_vs_PRJNA327437_Sscl1980_lesion_BnapDH12705_leaf_24hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA341340"
this_meta <- meta %>% filter(bioproject == bpr)
# this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA341340_Sscl1980_lesion_Slys", "PRJNA341340_Sscl1980_ball_IVPDB", "IPvsIV",
"PRJNA341340_Sscl1980_lesion_Hann", "PRJNA341340_Sscl1980_ball_IVPDB", "IPvsIV",
"PRJNA341340_Sscl1980_lesion_Atha", "PRJNA341340_Sscl1980_ball_IVPDB", "IPvsIV",
"PRJNA341340_Sscl1980_lesion_Slys", "PRJNA341340_Sscl1980_lesion_Hann", "SlysvsHann",
"PRJNA341340_Sscl1980_lesion_Slys", "PRJNA341340_Sscl1980_lesion_Atha", "SlysvsAtha",
"PRJNA341340_Sscl1980_lesion_Hann", "PRJNA341340_Sscl1980_lesion_Slys", "HannvsSlys",
"PRJNA341340_Sscl1980_lesion_Hann", "PRJNA341340_Sscl1980_lesion_Atha", "HannvsAtha",
"PRJNA341340_Sscl1980_lesion_Atha", "PRJNA341340_Sscl1980_lesion_Slys", "AthavsSlys",
"PRJNA341340_Sscl1980_lesion_Atha", "PRJNA341340_Sscl1980_lesion_Hann", "AthavsHann",
"PRJNA341340_Sscl1980_sclerotia_IV", "PRJNA341340_Sscl1980_ball_IVPDB", "Sclerotial"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 2940 rows containing non-finite values (`stat_bin()`).
## up down
## PRJNA341340_Sscl1980_lesion_Slys_vs_PRJNA341340_Sscl1980_ball_IVPDB 2022 2448
## PRJNA341340_Sscl1980_lesion_Hann_vs_PRJNA341340_Sscl1980_ball_IVPDB 2402 2950
## PRJNA341340_Sscl1980_lesion_Atha_vs_PRJNA341340_Sscl1980_ball_IVPDB 1782 2347
## PRJNA341340_Sscl1980_lesion_Slys_vs_PRJNA341340_Sscl1980_lesion_Hann 702 498
## PRJNA341340_Sscl1980_lesion_Slys_vs_PRJNA341340_Sscl1980_lesion_Atha 160 303
## PRJNA341340_Sscl1980_lesion_Hann_vs_PRJNA341340_Sscl1980_lesion_Slys 498 702
## PRJNA341340_Sscl1980_lesion_Hann_vs_PRJNA341340_Sscl1980_lesion_Atha 834 890
## PRJNA341340_Sscl1980_lesion_Atha_vs_PRJNA341340_Sscl1980_lesion_Slys 303 160
## PRJNA341340_Sscl1980_lesion_Atha_vs_PRJNA341340_Sscl1980_lesion_Hann 890 834
## PRJNA341340_Sscl1980_sclerotia_IV_vs_PRJNA341340_Sscl1980_ball_IVPDB 2902 3229
## zerocounts
## PRJNA341340_Sscl1980_lesion_Slys_vs_PRJNA341340_Sscl1980_ball_IVPDB 242
## PRJNA341340_Sscl1980_lesion_Hann_vs_PRJNA341340_Sscl1980_ball_IVPDB 242
## PRJNA341340_Sscl1980_lesion_Atha_vs_PRJNA341340_Sscl1980_ball_IVPDB 242
## PRJNA341340_Sscl1980_lesion_Slys_vs_PRJNA341340_Sscl1980_lesion_Hann 242
## PRJNA341340_Sscl1980_lesion_Slys_vs_PRJNA341340_Sscl1980_lesion_Atha 242
## PRJNA341340_Sscl1980_lesion_Hann_vs_PRJNA341340_Sscl1980_lesion_Slys 242
## PRJNA341340_Sscl1980_lesion_Hann_vs_PRJNA341340_Sscl1980_lesion_Atha 242
## PRJNA341340_Sscl1980_lesion_Atha_vs_PRJNA341340_Sscl1980_lesion_Slys 242
## PRJNA341340_Sscl1980_lesion_Atha_vs_PRJNA341340_Sscl1980_lesion_Hann 242
## PRJNA341340_Sscl1980_sclerotia_IV_vs_PRJNA341340_Sscl1980_ball_IVPDB 242
## n
## PRJNA341340_Sscl1980_lesion_Slys_vs_PRJNA341340_Sscl1980_ball_IVPDB 11130
## PRJNA341340_Sscl1980_lesion_Hann_vs_PRJNA341340_Sscl1980_ball_IVPDB 11130
## PRJNA341340_Sscl1980_lesion_Atha_vs_PRJNA341340_Sscl1980_ball_IVPDB 11130
## PRJNA341340_Sscl1980_lesion_Slys_vs_PRJNA341340_Sscl1980_lesion_Hann 11130
## PRJNA341340_Sscl1980_lesion_Slys_vs_PRJNA341340_Sscl1980_lesion_Atha 11130
## PRJNA341340_Sscl1980_lesion_Hann_vs_PRJNA341340_Sscl1980_lesion_Slys 11130
## PRJNA341340_Sscl1980_lesion_Hann_vs_PRJNA341340_Sscl1980_lesion_Atha 11130
## PRJNA341340_Sscl1980_lesion_Atha_vs_PRJNA341340_Sscl1980_lesion_Slys 11130
## PRJNA341340_Sscl1980_lesion_Atha_vs_PRJNA341340_Sscl1980_lesion_Hann 11130
## PRJNA341340_Sscl1980_sclerotia_IV_vs_PRJNA341340_Sscl1980_ball_IVPDB 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA418121"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA418121_Sscl1980_periphery_AthaCol0_leaf_50hpi", "PRJNA418121_Sscl1980_periphery_SlysHeinz_leaf_50hpi", "AthavsSlys",
"PRJNA418121_Sscl1980_periphery_SlysHeinz_leaf_50hpi", "PRJNA418121_Sscl1980_periphery_AthaCol0_leaf_50hpi", "SlysvsAtha"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1390 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA418121_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA418121_Sscl1980_periphery_SlysHeinz_leaf_50hpi 732
## PRJNA418121_Sscl1980_periphery_SlysHeinz_leaf_50hpi_vs_PRJNA418121_Sscl1980_periphery_AthaCol0_leaf_50hpi 649
## down
## PRJNA418121_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA418121_Sscl1980_periphery_SlysHeinz_leaf_50hpi 649
## PRJNA418121_Sscl1980_periphery_SlysHeinz_leaf_50hpi_vs_PRJNA418121_Sscl1980_periphery_AthaCol0_leaf_50hpi 732
## zerocounts
## PRJNA418121_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA418121_Sscl1980_periphery_SlysHeinz_leaf_50hpi 693
## PRJNA418121_Sscl1980_periphery_SlysHeinz_leaf_50hpi_vs_PRJNA418121_Sscl1980_periphery_AthaCol0_leaf_50hpi 693
## n
## PRJNA418121_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA418121_Sscl1980_periphery_SlysHeinz_leaf_50hpi 11130
## PRJNA418121_Sscl1980_periphery_SlysHeinz_leaf_50hpi_vs_PRJNA418121_Sscl1980_periphery_AthaCol0_leaf_50hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA471709"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA471709_Sscl1980_lesion_Gmax91145_stem_48hpi", "PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi", "MvsERes",
"PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi", "PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi", "LvsERes",
"PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi", "PRJNA471709_Sscl1980_lesion_Gmax91145_stem_48hpi", "LvsMRes",
"PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi", "PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi", "MvsE",
"PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi", "PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi", "LvsE",
"PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi", "PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi", "LvsM",
"PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi", "PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi", "SuscvsResE",
"PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi", "PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi", "SuscvsRes",
"PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi", "PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi", "SuscvsRes"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 4491 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA471709_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi 428
## PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi 908
## PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_48hpi 249
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi 352
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi 693
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi 126
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi 4
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi 268
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi 248
## down
## PRJNA471709_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi 305
## PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi 692
## PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_48hpi 160
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi 162
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi 541
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi 139
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi 4
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi 214
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi 289
## zerocounts
## PRJNA471709_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi 457
## PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi 457
## PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_48hpi 457
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi 457
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi 457
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi 457
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi 457
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi 457
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi 457
## n
## PRJNA471709_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi 11130
## PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi 11130
## PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_48hpi 11130
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi 11130
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi 11130
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi 11130
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_24hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_24hpi 11130
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi 11130
## PRJNA471709_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA471709_Sscl1980_lesion_Gmax91145_stem_96hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA477716"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi", "PRJNA477716_Sscl1980_ball_IVPDB_96hpi", "IPvsIV",
"PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi", "PRJNA477716_Sscl1980_ball_IVMM_96hpi", "IPvsIV",
"PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi", "PRJNA477716_Sscl1980_periphery_IVPDA_48hpi", "IPvsIV",
"PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi", "PRJNA477716_Sscl1980_center_IVPDA_48hpi", "IPvsIV"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 988 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_ball_IVPDB_96hpi 2148
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_ball_IVMM_96hpi 3211
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_periphery_IVPDA_48hpi 2328
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_center_IVPDA_48hpi 2105
## down
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_ball_IVPDB_96hpi 2756
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_ball_IVMM_96hpi 3199
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_periphery_IVPDA_48hpi 2112
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_center_IVPDA_48hpi 2216
## zerocounts
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_ball_IVPDB_96hpi 189
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_ball_IVMM_96hpi 189
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_periphery_IVPDA_48hpi 189
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_center_IVPDA_48hpi 189
## n
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_ball_IVPDB_96hpi 11130
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_ball_IVMM_96hpi 11130
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_periphery_IVPDA_48hpi 11130
## PRJNA477716_Sscl1980_center_AthaCol0_leaf_48hpi_vs_PRJNA477716_Sscl1980_center_IVPDA_48hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA501892"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi", "PRJNA501892_Sscl1980_colony_IVPDA", "IPvsIV",
"PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi", "PRJNA501892_Sscl1980_colony_IVPDA", "IPvsIV",
"PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi", "PRJNA501892_Sscl1980_colony_IVPDA", "IPvsIV",
"PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi", "PRJNA501892_Sscl1980_colony_IVPDA", "IPvsIV",
"PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi", "PRJNA501892_Sscl1980_colony_IVPDA", "IPvsIV",
"PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi", "PRJNA501892_Sscl1980_colony_IVPDA", "IPvsIV",
"PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi", "PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi", "IPvsIV",
"PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi", "PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi", "IPvsIV",
"PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi", "PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi", "IPvsIV",
"PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi", "PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi", "IPvsIV",
"PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi", "PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi", "IPvsIV",
"PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi", "PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi", "IPvsIV",
"PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi", "PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi", "SuscvsResE",
"PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi", "PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi", "SuscvsRes",
"PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi", "PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi", "SuscvsRes",
"PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi", "PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi", "MvsE",
"PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi", "PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi", "LvsE",
"PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi", "PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi", "LvsM",
"PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi", "PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi", "MvsERes",
"PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi", "PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi", "LvsERes",
"PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi", "PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi", "LvsMRes"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 7581 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 1828
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 1797
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 2195
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 1857
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 1749
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 2034
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi 451
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi 959
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi 289
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi 385
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi 752
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi 142
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi 1
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi 4
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi 341
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi1 385
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi1 752
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi1 142
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi1 451
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi1 959
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi1 289
## down
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 1516
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 1323
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 1739
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 1584
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 1228
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 1726
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi 352
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi 744
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi 187
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi 192
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi 611
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi 162
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi 4
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi 4
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi 368
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi1 192
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi1 611
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi1 162
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi1 352
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi1 744
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi1 187
## zerocounts
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 328
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 328
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 328
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 328
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 328
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 328
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi 328
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi 328
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi 328
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi 328
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi 328
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi 328
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi 328
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi 328
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi 328
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi1 328
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi1 328
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi1 328
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi1 328
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi1 328
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi1 328
## n
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 11130
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 11130
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 11130
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 11130
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 11130
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_colony_IVPDA 11130
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi 11130
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi 11130
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi 11130
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi 11130
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi 11130
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi 11130
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi 11130
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi 11130
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi 11130
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi1 11130
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_24hpi1 11130
## PRJNA501892_Sscl1980_lesion_Gmax9144_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax9144_stem_48hpi1 11130
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi1 11130
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_24hpi1 11130
## PRJNA501892_Sscl1980_lesion_Gmax91145_stem_96hpi_vs_PRJNA501892_Sscl1980_lesion_Gmax91145_stem_48hpi1 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA516496"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi", "PRJNA516496_SsclCU824_ball_IVPDB", "IPvsIV",
"PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi", "PRJNA516496_SsclCU824_ball_IVPDB", "IPvsIV",
"PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi", "PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi", "LangvsBnap",
"PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi", "PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi", "BnapvsLang"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1620 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi_vs_PRJNA516496_SsclCU824_ball_IVPDB 1652
## PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi_vs_PRJNA516496_SsclCU824_ball_IVPDB 1712
## PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi_vs_PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi 62
## PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi_vs_PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi 130
## down
## PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi_vs_PRJNA516496_SsclCU824_ball_IVPDB 1736
## PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi_vs_PRJNA516496_SsclCU824_ball_IVPDB 1850
## PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi_vs_PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi 130
## PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi_vs_PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi 62
## zerocounts
## PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi_vs_PRJNA516496_SsclCU824_ball_IVPDB 213
## PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi_vs_PRJNA516496_SsclCU824_ball_IVPDB 213
## PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi_vs_PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi 213
## PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi_vs_PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi 213
## n
## PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi_vs_PRJNA516496_SsclCU824_ball_IVPDB 11130
## PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi_vs_PRJNA516496_SsclCU824_ball_IVPDB 11130
## PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi_vs_PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi 11130
## PRJNA516496_SsclCU824_lesion_BnapCobbler_stem_72hpi_vs_PRJNA516496_SsclCU824_lesion_LangTanjil_stem_72hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA574280"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi", "PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi", "HannvsPvul",
"PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi", "PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi", "BvulvulvsPvul",
"PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi", "PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi", "RcomvsPvul",
"PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi", "PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi", "PvulvsHann",
"PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi", "PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi", "BvulvulvsHann",
"PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi", "PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi", "RcomvsHann",
"PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi", "PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi", "PvulvsBvulvul",
"PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi", "PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi", "HannvsBvulvul",
"PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi", "PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi", "RcomvsBvulvul",
"PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi", "PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi", "PvulvsRcom",
"PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi", "PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi", "HannvsRcom",
"PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi", "PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi", "BvulvulvsRcom"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 6660 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi_vs_PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi 780
## PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi 874
## PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi 1499
## PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi 1123
## PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi 2484
## PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi 2471
## PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi 573
## PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi_vs_PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi 1900
## PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi 2015
## PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi 1609
## PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi_vs_PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi 2039
## PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi 2290
## down
## PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi_vs_PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi 1123
## PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi 573
## PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi 1609
## PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi 780
## PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi 1900
## PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi 2039
## PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi 874
## PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi_vs_PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi 2484
## PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi 2290
## PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi 1499
## PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi_vs_PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi 2471
## PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi 2015
## zerocounts
## PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi_vs_PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi 507
## PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi 507
## PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi 507
## PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi 507
## PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi 507
## PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi 507
## PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi 507
## PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi_vs_PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi 507
## PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi 507
## PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi 507
## PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi_vs_PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi 507
## PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi 507
## n
## PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi_vs_PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi 11130
## PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi 11130
## PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi 11130
## PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi 11130
## PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi 11130
## PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi 11130
## PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi 11130
## PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi_vs_PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi 11130
## PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi 11130
## PRJNA574280_Sscl1980_periphery_PvulG19833_leaf_50hpi_vs_PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi 11130
## PRJNA574280_Sscl1980_periphery_HannXRQ_leaf_24hpi_vs_PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi 11130
## PRJNA574280_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA574280_Sscl1980_periphery_Rcom_leaf_50hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA577619"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_12hpi", "PRJNA577619_SsclDT8_surface_hyphae_Bnap_12hpi", "SsHADVvsVF",
"PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_24hpi", "PRJNA577619_SsclDT8_surface_hyphae_Bnap_24hpi", "SsHADVvsVF",
"PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_18hpi", "PRJNA577619_SsclDT8_surface_hyphae_Bnap_18hpi", "SsHADVvsVF"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 462 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_12hpi_vs_PRJNA577619_SsclDT8_surface_hyphae_Bnap_12hpi 2980
## PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_24hpi_vs_PRJNA577619_SsclDT8_surface_hyphae_Bnap_24hpi 3120
## PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_18hpi_vs_PRJNA577619_SsclDT8_surface_hyphae_Bnap_18hpi 3162
## down
## PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_12hpi_vs_PRJNA577619_SsclDT8_surface_hyphae_Bnap_12hpi 3128
## PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_24hpi_vs_PRJNA577619_SsclDT8_surface_hyphae_Bnap_24hpi 2944
## PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_18hpi_vs_PRJNA577619_SsclDT8_surface_hyphae_Bnap_18hpi 3131
## zerocounts
## PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_12hpi_vs_PRJNA577619_SsclDT8_surface_hyphae_Bnap_12hpi 152
## PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_24hpi_vs_PRJNA577619_SsclDT8_surface_hyphae_Bnap_24hpi 152
## PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_18hpi_vs_PRJNA577619_SsclDT8_surface_hyphae_Bnap_18hpi 152
## n
## PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_12hpi_vs_PRJNA577619_SsclDT8_surface_hyphae_Bnap_12hpi 11130
## PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_24hpi_vs_PRJNA577619_SsclDT8_surface_hyphae_Bnap_24hpi 11130
## PRJNA577619_SsclDT8SsHADV_surface_hyphae_Bnap_18hpi_vs_PRJNA577619_SsclDT8_surface_hyphae_Bnap_18hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA593737"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA593737_SsclZ113OXSsNSRV1_colony_IVPDA", "PRJNA593737_Sscl1980_colony_IVPDA", "SsNRSVvsVF",
"PRJNA593737_SsclZ11OXSsNSRV1_colony_IVPDA", "PRJNA593737_Sscl1980_colony_IVPDA", "SsNRSVvsVF",
"PRJNA593737_SsclAH98_colony_IVPDA", "PRJNA593737_Sscl1980_colony_IVPDA", "SsNRSVSsHVvsVF"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 375 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA593737_SsclZ113OXSsNSRV1_colony_IVPDA_vs_PRJNA593737_Sscl1980_colony_IVPDA 651
## PRJNA593737_SsclZ11OXSsNSRV1_colony_IVPDA_vs_PRJNA593737_Sscl1980_colony_IVPDA 2874
## PRJNA593737_SsclAH98_colony_IVPDA_vs_PRJNA593737_Sscl1980_colony_IVPDA 2961
## down
## PRJNA593737_SsclZ113OXSsNSRV1_colony_IVPDA_vs_PRJNA593737_Sscl1980_colony_IVPDA 1163
## PRJNA593737_SsclZ11OXSsNSRV1_colony_IVPDA_vs_PRJNA593737_Sscl1980_colony_IVPDA 2922
## PRJNA593737_SsclAH98_colony_IVPDA_vs_PRJNA593737_Sscl1980_colony_IVPDA 3178
## zerocounts
## PRJNA593737_SsclZ113OXSsNSRV1_colony_IVPDA_vs_PRJNA593737_Sscl1980_colony_IVPDA 115
## PRJNA593737_SsclZ11OXSsNSRV1_colony_IVPDA_vs_PRJNA593737_Sscl1980_colony_IVPDA 115
## PRJNA593737_SsclAH98_colony_IVPDA_vs_PRJNA593737_Sscl1980_colony_IVPDA 115
## n
## PRJNA593737_SsclZ113OXSsNSRV1_colony_IVPDA_vs_PRJNA593737_Sscl1980_colony_IVPDA 11130
## PRJNA593737_SsclZ11OXSsNSRV1_colony_IVPDA_vs_PRJNA593737_Sscl1980_colony_IVPDA 11130
## PRJNA593737_SsclAH98_colony_IVPDA_vs_PRJNA593737_Sscl1980_colony_IVPDA 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA601001"
this_meta <- meta %>% filter(bioproject == bpr)
# Just the counts please, no replicates
results <- run_count_norm(lcounts, bpr)
## converting counts to integer mode
norm_counts[[bpr]] <- results[["norm_counts"]]
bpr <- "PRJNA603456"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA603456_SsclMBGSs2_colony_IVPDAindol3carbinol", "PRJNA603456_SsclMBGSs2_colony_IVPDA", "none",
"PRJNA603456_SsclMBGSs2_colony_IVPDAallylisothiocyanate", "PRJNA603456_SsclMBGSs2_colony_IVPDA", "none"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 984 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA603456_SsclMBGSs2_colony_IVPDAindol3carbinol_vs_PRJNA603456_SsclMBGSs2_colony_IVPDA 47
## PRJNA603456_SsclMBGSs2_colony_IVPDAallylisothiocyanate_vs_PRJNA603456_SsclMBGSs2_colony_IVPDA 399
## down
## PRJNA603456_SsclMBGSs2_colony_IVPDAindol3carbinol_vs_PRJNA603456_SsclMBGSs2_colony_IVPDA 116
## PRJNA603456_SsclMBGSs2_colony_IVPDAallylisothiocyanate_vs_PRJNA603456_SsclMBGSs2_colony_IVPDA 434
## zerocounts
## PRJNA603456_SsclMBGSs2_colony_IVPDAindol3carbinol_vs_PRJNA603456_SsclMBGSs2_colony_IVPDA 492
## PRJNA603456_SsclMBGSs2_colony_IVPDAallylisothiocyanate_vs_PRJNA603456_SsclMBGSs2_colony_IVPDA 492
## n
## PRJNA603456_SsclMBGSs2_colony_IVPDAindol3carbinol_vs_PRJNA603456_SsclMBGSs2_colony_IVPDA 11130
## PRJNA603456_SsclMBGSs2_colony_IVPDAallylisothiocyanate_vs_PRJNA603456_SsclMBGSs2_colony_IVPDA 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA607858"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA607858_Sscl_lesion_AthaCol0Ago21_leaf_8hpi", "PRJNA607858_Sscl_lesion_AthaCol0_leaf_8hpi", "none"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1784 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA607858_Sscl_lesion_AthaCol0Ago21_leaf_8hpi_vs_PRJNA607858_Sscl_lesion_AthaCol0_leaf_8hpi 1
## down
## PRJNA607858_Sscl_lesion_AthaCol0Ago21_leaf_8hpi_vs_PRJNA607858_Sscl_lesion_AthaCol0_leaf_8hpi 8
## zerocounts
## PRJNA607858_Sscl_lesion_AthaCol0Ago21_leaf_8hpi_vs_PRJNA607858_Sscl_lesion_AthaCol0_leaf_8hpi 1784
## n
## PRJNA607858_Sscl_lesion_AthaCol0Ago21_leaf_8hpi_vs_PRJNA607858_Sscl_lesion_AthaCol0_leaf_8hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA641217"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA641217_SsclDT8_lesion_Bnap_hypocotyl", "PRJNA641217_SsclDT8_colony_IVPDA", "IPvsIV",
"PRJNA641217_SsclDT8SsHADV_lesion_Bnap_hypocotyl", "PRJNA641217_SsclDT8SsHADV_colony_IVPDA", "none",
"PRJNA641217_SsclDT8SsHADV_colony_IVPDA", "PRJNA641217_SsclDT8_colony_IVPDA", "SsHADVvsVF",
"PRJNA641217_SsclDT8SsHADV_lesion_Bnap_hypocotyl", "PRJNA641217_SsclDT8_lesion_Bnap_hypocotyl", "SsHADVvsVF"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 2060 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA641217_SsclDT8_lesion_Bnap_hypocotyl_vs_PRJNA641217_SsclDT8_colony_IVPDA 2179
## PRJNA641217_SsclDT8SsHADV_lesion_Bnap_hypocotyl_vs_PRJNA641217_SsclDT8SsHADV_colony_IVPDA 2054
## PRJNA641217_SsclDT8SsHADV_colony_IVPDA_vs_PRJNA641217_SsclDT8_colony_IVPDA 3014
## PRJNA641217_SsclDT8SsHADV_lesion_Bnap_hypocotyl_vs_PRJNA641217_SsclDT8_lesion_Bnap_hypocotyl 2688
## down
## PRJNA641217_SsclDT8_lesion_Bnap_hypocotyl_vs_PRJNA641217_SsclDT8_colony_IVPDA 2199
## PRJNA641217_SsclDT8SsHADV_lesion_Bnap_hypocotyl_vs_PRJNA641217_SsclDT8SsHADV_colony_IVPDA 2039
## PRJNA641217_SsclDT8SsHADV_colony_IVPDA_vs_PRJNA641217_SsclDT8_colony_IVPDA 3002
## PRJNA641217_SsclDT8SsHADV_lesion_Bnap_hypocotyl_vs_PRJNA641217_SsclDT8_lesion_Bnap_hypocotyl 2513
## zerocounts
## PRJNA641217_SsclDT8_lesion_Bnap_hypocotyl_vs_PRJNA641217_SsclDT8_colony_IVPDA 508
## PRJNA641217_SsclDT8SsHADV_lesion_Bnap_hypocotyl_vs_PRJNA641217_SsclDT8SsHADV_colony_IVPDA 508
## PRJNA641217_SsclDT8SsHADV_colony_IVPDA_vs_PRJNA641217_SsclDT8_colony_IVPDA 508
## PRJNA641217_SsclDT8SsHADV_lesion_Bnap_hypocotyl_vs_PRJNA641217_SsclDT8_lesion_Bnap_hypocotyl 508
## n
## PRJNA641217_SsclDT8_lesion_Bnap_hypocotyl_vs_PRJNA641217_SsclDT8_colony_IVPDA 11130
## PRJNA641217_SsclDT8SsHADV_lesion_Bnap_hypocotyl_vs_PRJNA641217_SsclDT8SsHADV_colony_IVPDA 11130
## PRJNA641217_SsclDT8SsHADV_colony_IVPDA_vs_PRJNA641217_SsclDT8_colony_IVPDA 11130
## PRJNA641217_SsclDT8SsHADV_lesion_Bnap_hypocotyl_vs_PRJNA641217_SsclDT8_lesion_Bnap_hypocotyl 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA641462"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA641462_SsclDT8SsHADV_lesion_BnapHuashaung4_hypocotyl", "PRJNA641462_SsclDT8_lesion_BnapHuashaung4_hypocotyl", "SsHADVvsVF"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 947 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA641462_SsclDT8SsHADV_lesion_BnapHuashaung4_hypocotyl_vs_PRJNA641462_SsclDT8_lesion_BnapHuashaung4_hypocotyl 2180
## down
## PRJNA641462_SsclDT8SsHADV_lesion_BnapHuashaung4_hypocotyl_vs_PRJNA641462_SsclDT8_lesion_BnapHuashaung4_hypocotyl 1961
## zerocounts
## PRJNA641462_SsclDT8SsHADV_lesion_BnapHuashaung4_hypocotyl_vs_PRJNA641462_SsclDT8_lesion_BnapHuashaung4_hypocotyl 947
## n
## PRJNA641462_SsclDT8SsHADV_lesion_BnapHuashaung4_hypocotyl_vs_PRJNA641462_SsclDT8_lesion_BnapHuashaung4_hypocotyl 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA643804"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
"PRJNA643804_SsclDK3SlaGemV1_cellophane_colony_IVPDACellophane", "PRJNA643804_SsclDK3_cellophane_colony_IVPDACellophane", "SlaGemV1vsVF"
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 289 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA643804_SsclDK3SlaGemV1_cellophane_colony_IVPDACellophane_vs_PRJNA643804_SsclDK3_cellophane_colony_IVPDACellophane 1868
## down
## PRJNA643804_SsclDK3SlaGemV1_cellophane_colony_IVPDACellophane_vs_PRJNA643804_SsclDK3_cellophane_colony_IVPDACellophane 1810
## zerocounts
## PRJNA643804_SsclDK3SlaGemV1_cellophane_colony_IVPDACellophane_vs_PRJNA643804_SsclDK3_cellophane_colony_IVPDACellophane 274
## n
## PRJNA643804_SsclDK3SlaGemV1_cellophane_colony_IVPDACellophane_vs_PRJNA643804_SsclDK3_cellophane_colony_IVPDACellophane 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA647983"
this_meta <- meta %>% filter(bioproject == bpr)
this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
## # A tibble: 3 × 13
## included sample organism accession host host_accession tissue hpi
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 NETWORK_ONLY PRJNA647983… Sclerot… CU11.19 in v… <NA> colony NA
## 2 NETWORK_ONLY PRJNA647983… Sclerot… F5 in v… <NA> colony NA
## 3 NETWORK_ONLY PRJNA647983… Sclerot… F4 in v… <NA> colony NA
## # ℹ 5 more variables: plant_tissue <chr>, sample_material <chr>, notes <chr>,
## # host_ <chr>, organism_ <chr>
# Network only
results <- run_count_norm(lcounts, bpr)
## converting counts to integer mode
norm_counts[[bpr]] <- results[["norm_counts"]]
bpr <- "PRJNA670487"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
'PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_ball_IVPDB', 'IPvsIV',
'PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_ball_IVPDB', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_ball_IVPDB', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_ball_IVPDB', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_ball_IVPDB', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_ball_IVPDB', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_ball_IVPDB', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_ball_IVPDB', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_ball_IVPDB', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_ball_IVPDB', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_ball_IVPDB', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_ball_IVPDB', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_colony_IVPDA_DMSO', 'IPvsIV',
'PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_colony_IVPDA_DMSO', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_colony_IVPDA_DMSO', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_colony_IVPDA_DMSO', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_colony_IVPDA_DMSO', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_colony_IVPDA_DMSO', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_colony_IVPDA_DMSO', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_colony_IVPDA_DMSO', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_colony_IVPDA_DMSO', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_colony_IVPDA_DMSO', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_colony_IVPDA_DMSO', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_colony_IVPDA_DMSO', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_IVPDA', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_IVPDA', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_periphery_IVPDA', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_periphery_IVPDA', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_IVPDA', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_IVPDA', 'IPvsIV',
'PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_center_IVPDA', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_center_IVPDA', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_center_IVPDA', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_center_IVPDA', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_center_IVPDA', 'IPvsIV',
'PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_center_IVPDA', 'IPvsIV',
'PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi', 'SlysvsAtha',
'PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi', 'HannvsAtha',
'PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi', 'BvulvulvsAtha',
'PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi', 'RcomvsAtha',
'PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi', 'PvulvsAtha',
'PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi', 'AthavsSlys',
'PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi', 'HannvsSlys',
'PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi', 'BvulvulvsSlys',
'PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi', 'RcomvsSlys',
'PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi', 'PvulvsSlys',
'PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi', 'AthavsHann',
'PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi', 'SlysvsHann',
'PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi', 'BvulvulvsHann',
'PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi', 'RcomvsHann',
'PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi', 'PvulvsHann',
'PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi', 'AthavsBvulvul',
'PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi', 'SlysvsBvulvul',
'PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi', 'HannvsBvulvul',
'PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi', 'RcomvsBvulvul',
'PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi', 'PvulvsBvulvul',
'PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi', 'AthavsRcom',
'PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi', 'SlysvsRcom',
'PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi', 'HannvsRcom',
'PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi', 'BvulvulvsRcom',
'PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi', 'PvulvsRcom',
'PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi', 'AthavsPvul',
'PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi', 'SlysvsPvul',
'PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi', 'HannvsPvul',
'PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi', 'BvulvulvsPvul',
'PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi', 'RcomvsPvul',
'PRJNA670487_Sscl1980_center_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi', 'SlysvsAtha',
'PRJNA670487_Sscl1980_center_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi', 'HannvsAtha',
'PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi', 'BvulvulvsAtha',
'PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi', 'RcomvsAtha',
'PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi', 'PvulvsAtha',
'PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Slys_leaf_50hpi', 'AthavsSlys',
'PRJNA670487_Sscl1980_center_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_center_Slys_leaf_50hpi', 'HannvsSlys',
'PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_center_Slys_leaf_50hpi', 'BvulvulvsSlys',
'PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Slys_leaf_50hpi', 'RcomvsSlys',
'PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Slys_leaf_50hpi', 'PvulvsSlys',
'PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Hann_leaf_24hpi', 'AthavsHann',
'PRJNA670487_Sscl1980_center_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Hann_leaf_24hpi', 'SlysvsHann',
'PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_center_Hann_leaf_24hpi', 'BvulvulvsHann',
'PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Hann_leaf_24hpi', 'RcomvsHann',
'PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Hann_leaf_24hpi', 'PvulvsHann',
'PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi', 'AthavsBvulvul',
'PRJNA670487_Sscl1980_center_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi', 'SlysvsBvulvul',
'PRJNA670487_Sscl1980_center_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi', 'HannvsBvulvul',
'PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi', 'RcomvsBvulvul',
'PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi', 'PvulvsBvulvul',
'PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi', 'AthavsRcom',
'PRJNA670487_Sscl1980_center_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi', 'SlysvsRcom',
'PRJNA670487_Sscl1980_center_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi', 'HannvsRcom',
'PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi', 'BvulvulvsRcom',
'PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi', 'PvulvsRcom',
'PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi', 'AthavsPvul',
'PRJNA670487_Sscl1980_center_Slys_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi', 'SlysvsPvul',
'PRJNA670487_Sscl1980_center_Hann_leaf_24hpi', 'PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi', 'HannvsPvul',
'PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi', 'PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi', 'BvulvulvsPvul',
'PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi', 'PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi', 'RcomvsPvul'
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## rlog() may take a few minutes with 30 or more samples,
## vst() is a much faster transformation
## rlog() may take a few minutes with 30 or more samples,
## vst() is a much faster transformation
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 11520 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 1546
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 1545
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 1676
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 1514
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 1937
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 2683
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 2120
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 2152
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 2645
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 2454
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 1703
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 1847
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 1088
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 1857
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 981
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 1617
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 979
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 2762
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 2412
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 2694
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 2737
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 2675
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 1493
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 2173
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 1598
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 1421
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 1004
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 3022
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 3020
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 1984
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 1583
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 1674
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 2491
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 3062
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 2724
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 2365
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 87
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 377
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 1725
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 2301
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 472
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 233
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 193
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 1778
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 2239
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 558
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 754
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 535
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 2692
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 2778
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 1249
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 1225
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 1302
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 1969
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 2140
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 836
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 2061
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 1911
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 2121
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 2359
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 1659
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 305
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 263
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 645
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 1029
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 1694
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 2206
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 2124
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 2003
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 965
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 147
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 2013
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 1466
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 1706
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 496
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 2430
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 2266
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 3488
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 2030
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 2741
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 1394
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 890
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 2412
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 1926
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 252
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 1932
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 1664
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 1579
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 2350
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 1535
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 421
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 144
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 2016
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 381
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 1341
## down
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 2457
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 2470
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 2614
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 2336
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 3182
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 3642
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 2551
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 2247
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 3294
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 3218
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 2291
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 2165
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 838
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 1698
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 825
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 1296
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 1244
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 2780
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 1959
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 1935
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 2377
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 2560
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 968
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 1525
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 1547
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 1347
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 958
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 2543
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 2599
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 1797
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 1585
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 1582
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 2515
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 2476
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 2856
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 2101
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 233
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 754
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 1225
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 2061
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 305
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 87
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 535
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 1302
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 1911
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 263
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 377
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 193
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 1969
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 2121
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 645
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 1725
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 1778
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 2692
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 2359
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 1029
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 2301
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 2239
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 2778
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 2140
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 1694
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 472
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 558
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 1249
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 836
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 1659
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 147
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 2430
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 1394
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 1932
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 421
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 2266
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 890
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 1664
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 144
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 2206
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 2013
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 2412
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 1579
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 2016
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 2124
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 1466
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 3488
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 2350
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 381
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 2003
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 1706
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 2030
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 1926
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 1341
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 965
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 496
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 2741
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 252
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 1535
## zerocounts
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 116
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 116
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 116
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 116
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 116
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 116
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 116
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 116
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 116
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 116
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 116
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 116
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 116
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 116
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 116
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 116
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 116
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 116
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 116
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 116
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 116
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 116
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 116
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 116
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 116
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 116
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 116
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 116
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 116
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 116
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 116
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 116
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 116
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 116
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 116
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 116
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 116
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 116
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 116
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 116
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 116
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 116
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 116
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 116
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 116
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 116
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 116
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 116
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 116
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 116
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 116
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 116
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 116
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 116
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 116
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 116
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 116
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 116
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 116
## n
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 11130
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 11130
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 11130
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 11130
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 11130
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 11130
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 11130
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 11130
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 11130
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 11130
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 11130
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_ball_IVPDB 11130
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 11130
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 11130
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 11130
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 11130
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 11130
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 11130
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 11130
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 11130
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 11130
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 11130
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 11130
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_colony_IVPDA_DMSO 11130
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 11130
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 11130
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 11130
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 11130
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 11130
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_IVPDA 11130
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 11130
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 11130
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 11130
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 11130
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 11130
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_IVPDA 11130
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 11130
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 11130
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 11130
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 11130
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi 11130
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 11130
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 11130
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 11130
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 11130
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi 11130
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 11130
## PRJNA670487_Sscl1980_periphery_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_periphery_Pvul_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Slys_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 11130
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 11130
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 11130
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 11130
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Hann_leaf_24hpi 11130
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 11130
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 11130
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 11130
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 11130
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi 11130
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Slys_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Hann_leaf_24hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 11130
## PRJNA670487_Sscl1980_center_Rcom_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Pvul_leaf_50hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA687280"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi', 'PRJNA687280_SsclCU820_ball_IVPDB', 'IPvsIV',
'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi', 'PRJNA687280_SsclCU820_ball_IVPDB', 'IPvsIV',
'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi', 'PRJNA687280_SsclCU820_ball_IVPDB', 'IPvsIV',
'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi', 'PRJNA687280_SsclCU820_ball_IVPDB', 'IPvsIV',
'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi', 'PRJNA687280_SsclCU820_ball_IVPDB', 'IPvsIV',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi', 'PRJNA687280_SsclCU820_ball_IVPDB', 'IPvsIV',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi', 'PRJNA687280_SsclCU820_ball_IVPDB', 'IPvsIV',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi', 'PRJNA687280_SsclCU820_ball_IVPDB', 'IPvsIV',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi', 'PRJNA687280_SsclCU820_ball_IVPDB', 'IPvsIV',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi', 'PRJNA687280_SsclCU820_ball_IVPDB', 'IPvsIV',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi', 'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi', 'SuscvsResE',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi', 'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi', 'SuscvsResE',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi', 'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi', 'SuscvsRes',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi', 'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi', 'SuscvsRes',
'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi', 'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi', 'MvsERes',
'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi', 'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi', 'MvsERes',
'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi', 'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi', 'LvsERes',
'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi', 'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi', 'MvsERes',
'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi', 'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi', 'MvsERes',
'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi', 'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi', 'LvsERes',
'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi', 'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi', 'LvsMRes',
'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi', 'PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi', 'LvsMRes',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi', 'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi', 'MvsE',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi', 'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi', 'MvsE',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi', 'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi', 'LvsE',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi', 'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi', 'MvsE',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi', 'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi', 'MvsE',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi', 'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi', 'LvsE',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi', 'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi', 'LvsM',
'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi', 'PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi', 'LvsM'
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## rlog() may take a few minutes with 30 or more samples,
## vst() is a much faster transformation
## rlog() may take a few minutes with 30 or more samples,
## vst() is a much faster transformation
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3690 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 2958
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 3196
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 3099
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 3617
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 3405
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 3669
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 3370
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 3085
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 3087
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 2807
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 388
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 137
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi 23
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi 86
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 1780
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 2403
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 2115
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 1560
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 2797
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 2318
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi 2411
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi 1
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi 1145
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi 1216
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi 1762
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi 1816
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi 1725
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi 2181
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi 3393
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi 329
## down
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 4070
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 4446
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 4881
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 4102
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 4326
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 4201
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 4400
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 4839
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 4191
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 3583
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 273
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 160
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi 10
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi 424
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 2219
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 1899
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 1859
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 2093
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 2121
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 1961
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi 1414
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi 5
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi 1416
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi 1124
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi 1529
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi 2148
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi 1606
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi 1663
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi 2085
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi 101
## zerocounts
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 120
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 120
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 120
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 120
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi 120
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 120
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 120
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 120
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 120
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 120
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 120
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi 120
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi 120
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi 120
## n
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 11130
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 11130
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 11130
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 11130
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi_vs_PRJNA687280_SsclCU820_ball_IVPDB 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi 11130
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 11130
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 11130
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_6hpi 11130
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 11130
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 11130
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_12hpi 11130
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_24hpi 11130
## PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariHatTrick_stem_48hpi 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_6hpi 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_12hpi 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_24hpi 11130
## PRJNA687280_SsclCU820_lesion_CariKyabra_stem_72hpi_vs_PRJNA687280_SsclCU820_lesion_CariKyabra_stem_48hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA695466"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
'PRJNA695466_SsclDT8SsHADV_colony_IVPDA', 'PRJNA695466_SsclDT8_colony_IVPDA', 'SsHADVvsVF'
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 580 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA695466_SsclDT8SsHADV_colony_IVPDA_vs_PRJNA695466_SsclDT8_colony_IVPDA 3196
## down
## PRJNA695466_SsclDT8SsHADV_colony_IVPDA_vs_PRJNA695466_SsclDT8_colony_IVPDA 3204
## zerocounts
## PRJNA695466_SsclDT8SsHADV_colony_IVPDA_vs_PRJNA695466_SsclDT8_colony_IVPDA 577
## n
## PRJNA695466_SsclDT8SsHADV_colony_IVPDA_vs_PRJNA695466_SsclDT8_colony_IVPDA 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA706136"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
'PRJNA706136_Sscl_lesion_Bole_petiole_8hpi', 'PRJNA706136_Sscl_lesion_Bvil_petiole_8hpi', 'BolevsBvil',
'PRJNA706136_Sscl_lesion_Bvil_petiole_8hpi', 'PRJNA706136_Sscl_lesion_Bole_petiole_8hpi', 'BvilvsBole'
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1860 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA706136_Sscl_lesion_Bole_petiole_8hpi_vs_PRJNA706136_Sscl_lesion_Bvil_petiole_8hpi 80
## PRJNA706136_Sscl_lesion_Bvil_petiole_8hpi_vs_PRJNA706136_Sscl_lesion_Bole_petiole_8hpi 267
## down
## PRJNA706136_Sscl_lesion_Bole_petiole_8hpi_vs_PRJNA706136_Sscl_lesion_Bvil_petiole_8hpi 267
## PRJNA706136_Sscl_lesion_Bvil_petiole_8hpi_vs_PRJNA706136_Sscl_lesion_Bole_petiole_8hpi 80
## zerocounts
## PRJNA706136_Sscl_lesion_Bole_petiole_8hpi_vs_PRJNA706136_Sscl_lesion_Bvil_petiole_8hpi 930
## PRJNA706136_Sscl_lesion_Bvil_petiole_8hpi_vs_PRJNA706136_Sscl_lesion_Bole_petiole_8hpi 930
## n
## PRJNA706136_Sscl_lesion_Bole_petiole_8hpi_vs_PRJNA706136_Sscl_lesion_Bvil_petiole_8hpi 11130
## PRJNA706136_Sscl_lesion_Bvil_petiole_8hpi_vs_PRJNA706136_Sscl_lesion_Bole_petiole_8hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA735329"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
'PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_24hpi', 'PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_6hpi', 'MvsERes',
'PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_48hpi', 'PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_6hpi', 'LvsERes',
'PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_48hpi', 'PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_24hpi', 'LvsMRes'
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1671 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_24hpi_vs_PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_6hpi 1112
## PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_48hpi_vs_PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_6hpi 1633
## PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_48hpi_vs_PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_24hpi 1051
## down
## PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_24hpi_vs_PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_6hpi 881
## PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_48hpi_vs_PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_6hpi 1221
## PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_48hpi_vs_PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_24hpi 783
## zerocounts
## PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_24hpi_vs_PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_6hpi 555
## PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_48hpi_vs_PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_6hpi 555
## PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_48hpi_vs_PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_24hpi 555
## n
## PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_24hpi_vs_PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_6hpi 11130
## PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_48hpi_vs_PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_6hpi 11130
## PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_48hpi_vs_PRJNA735329_Sscl1980_lesion_BnapNingRS1_leaf_24hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA744751"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
# Just the counts please
results <- run_count_norm(lcounts, bpr)
## converting counts to integer mode
norm_counts[[bpr]] <- results[["norm_counts"]]
bpr <- "PRJNA766564"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
exclude <- c("SRR16082437")
contrasts <- matrix(c(
'PRJNA766564_Sscl_lesion_BnapWestar_stem_168hpi', 'PRJNA766564_Sscl_lesion_BnapWestar_stem_24hpi', 'LvsE',
'PRJNA766564_Sscl_lesion_BnapWestar_stem_168hpi', 'PRJNA766564_Sscl_lesion_Bnap1703_stem_168hpi', 'SuscvsRes'
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr, exclude=exclude)
## converting counts to integer mode
## Warning in rownames(this_meta) == colnames(this_counts): longer object length
## is not a multiple of shorter object length
## converting counts to integer mode
## factor levels were dropped which had no samples
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 2914 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA766564_Sscl_lesion_BnapWestar_stem_168hpi_vs_PRJNA766564_Sscl_lesion_BnapWestar_stem_24hpi 1323
## PRJNA766564_Sscl_lesion_BnapWestar_stem_168hpi_vs_PRJNA766564_Sscl_lesion_Bnap1703_stem_168hpi 6
## down
## PRJNA766564_Sscl_lesion_BnapWestar_stem_168hpi_vs_PRJNA766564_Sscl_lesion_BnapWestar_stem_24hpi 889
## PRJNA766564_Sscl_lesion_BnapWestar_stem_168hpi_vs_PRJNA766564_Sscl_lesion_Bnap1703_stem_168hpi 1
## zerocounts
## PRJNA766564_Sscl_lesion_BnapWestar_stem_168hpi_vs_PRJNA766564_Sscl_lesion_BnapWestar_stem_24hpi 1457
## PRJNA766564_Sscl_lesion_BnapWestar_stem_168hpi_vs_PRJNA766564_Sscl_lesion_Bnap1703_stem_168hpi 1457
## n
## PRJNA766564_Sscl_lesion_BnapWestar_stem_168hpi_vs_PRJNA766564_Sscl_lesion_BnapWestar_stem_24hpi 11130
## PRJNA766564_Sscl_lesion_BnapWestar_stem_168hpi_vs_PRJNA766564_Sscl_lesion_Bnap1703_stem_168hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA777355"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi', 'PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane', 'IPvsIV',
'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi', 'PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane', 'IPvsIV',
'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi', 'PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane', 'IPvsIV',
'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi', 'PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane', 'IPvsIV',
'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi', 'PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane', 'IPvsIV',
'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi', 'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi', 'MvsE',
'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi', 'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi', 'MvsE',
'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi', 'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi', 'MvsE',
'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi', 'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi', 'MvsE',
'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi', 'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi', 'MvsE',
'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi', 'PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi', 'MvsE'
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3201 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 2893
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 4053
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 3011
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 2498
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 3536
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi 2204
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi 2494
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi 3448
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi 3402
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi 3578
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi 3398
## down
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 2530
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 3635
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 2936
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 2724
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 2940
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi 1895
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi 2547
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi 3304
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi 3596
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi 3792
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi 3417
## zerocounts
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 289
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 289
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 289
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 289
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 289
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi 289
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi 289
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi 289
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi 289
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi 289
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi 289
## n
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 11130
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 11130
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 11130
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 11130
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi_vs_PRJNA777355_Sscl1980_cellophane_colony_IVPDACellophane 11130
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi 11130
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi 11130
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_2hpi 11130
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_12hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi 11130
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_24hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi 11130
## PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_36hpi_vs_PRJNA777355_Sscl1980_lesion_BnapWestar_leaf_6hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA789389"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
'PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_4hpi', 'PRJNA789389_SsclNB5_ball_IVCYM', 'IPvsIV',
'PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_8hpi', 'PRJNA789389_SsclNB5_ball_IVCYM', 'IPvsIV',
'PRJNA789389_SsclNB5_lesion_GmaxACColibri_leaf_8hpi', 'PRJNA789389_SsclNB5_ball_IVCYM', 'IPvsIV',
'PRJNA789389_SsclNB5_lesion_GmaxACColibri_leaf_8hpi', 'PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_8hpi', 'SuscvsResE'
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr, exclude = "SRR17267260")
## converting counts to integer mode
## Warning in rownames(this_meta) == colnames(this_counts): longer object length
## is not a multiple of shorter object length
## converting counts to integer mode
## factor levels were dropped which had no samples
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 2116 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_4hpi_vs_PRJNA789389_SsclNB5_ball_IVCYM 1444
## PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_8hpi_vs_PRJNA789389_SsclNB5_ball_IVCYM 1744
## PRJNA789389_SsclNB5_lesion_GmaxACColibri_leaf_8hpi_vs_PRJNA789389_SsclNB5_ball_IVCYM 1782
## PRJNA789389_SsclNB5_lesion_GmaxACColibri_leaf_8hpi_vs_PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_8hpi 0
## down
## PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_4hpi_vs_PRJNA789389_SsclNB5_ball_IVCYM 1557
## PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_8hpi_vs_PRJNA789389_SsclNB5_ball_IVCYM 1843
## PRJNA789389_SsclNB5_lesion_GmaxACColibri_leaf_8hpi_vs_PRJNA789389_SsclNB5_ball_IVCYM 1722
## PRJNA789389_SsclNB5_lesion_GmaxACColibri_leaf_8hpi_vs_PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_8hpi 2
## zerocounts
## PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_4hpi_vs_PRJNA789389_SsclNB5_ball_IVCYM 329
## PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_8hpi_vs_PRJNA789389_SsclNB5_ball_IVCYM 329
## PRJNA789389_SsclNB5_lesion_GmaxACColibri_leaf_8hpi_vs_PRJNA789389_SsclNB5_ball_IVCYM 329
## PRJNA789389_SsclNB5_lesion_GmaxACColibri_leaf_8hpi_vs_PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_8hpi 329
## n
## PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_4hpi_vs_PRJNA789389_SsclNB5_ball_IVCYM 11130
## PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_8hpi_vs_PRJNA789389_SsclNB5_ball_IVCYM 11130
## PRJNA789389_SsclNB5_lesion_GmaxACColibri_leaf_8hpi_vs_PRJNA789389_SsclNB5_ball_IVCYM 11130
## PRJNA789389_SsclNB5_lesion_GmaxACColibri_leaf_8hpi_vs_PRJNA789389_SsclNB5_lesion_GmaxACColibriOA_leaf_8hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA804213"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
# These don't have phenotypes that I can find, so no contrasts
# PRJNA804213_SsclP7_lesion_LsatPI251246_leaf_42hpi
# PRJNA804213_SsclP7_lesion_LsatArmenianLser_leaf_42hpi
# PRJNA804213_SsclP7_lesion_Lsat43_leaf_42hpi
contrasts <- matrix(c(
'PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi', 'SuscvsRes',
'PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi', 'PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi', 'SuscvsRes'
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## rlog() may take a long time with 50 or more samples,
## vst() is a much faster transformation
## rlog() may take a long time with 50 or more samples,
## vst() is a much faster transformation
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 16744 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 6
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 23
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 21
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 12
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 37
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 71
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 514
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 491
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 6
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 2
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 34
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 143
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 10
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 161
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 9
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 8
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 501
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 529
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 5
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 28
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 2
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 31
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 2
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 2
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 3
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 2
## down
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 46
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 25
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 21
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 274
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 279
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 19
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 33
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 127
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 106
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 73
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 13
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 16
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 109
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 172
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 150
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 10
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 618
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 7
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 600
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 22
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 9
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 218
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 319
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 117
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 5
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 264
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 94
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 74
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 528
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 502
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 112
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 118
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 17
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 112
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 8
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 39
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 6
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 6
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 1
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 0
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 5
## zerocounts
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 298
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 298
## n
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatPrazan_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatIceberg_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatBataviaTezier_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatRedGranoble_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_Lsat58_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatPallone_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatOakLeaf_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatKaiserSelbstschluss_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatKahu_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatCobhamGreen_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatBloodyWarrior_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatAmbassador_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatAdriatica2_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_Lsat68_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatSimpson_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatSaladin_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 11130
## PRJNA804213_SsclP7_lesion_LsatRomainDeBenicardo_leaf_42hpi_vs_PRJNA804213_SsclP7_lesion_LsatAspen_leaf_42hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA830457"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
# I decided to exclude the resynthesized sample in case it isn't a good representative of Bnapus
# PRJNA830457_Sscl_periphery_BnapResynthesized_leaf_24hpi
contrasts <- matrix(c(
'PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi', 'PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi', 'BrapvsBole',
'PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi', 'PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi', 'BnapvsBole',
'PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi', 'PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi', 'BrapvsBnap',
'PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi', 'PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi', 'BolevsBnap',
'PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi', 'PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi', 'BnapvsBrap',
'PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi', 'PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi', 'BolevsBrap'
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 2424 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi 1383
## PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi 142
## PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi 1186
## PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi 250
## PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi 779
## PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi 753
## down
## PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi 753
## PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi 250
## PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi 779
## PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi 142
## PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi 1186
## PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi 1383
## zerocounts
## PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi 393
## PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi 393
## PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi 393
## PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi 393
## PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi 393
## PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi 393
## n
## PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi 11130
## PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi 11130
## PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi 11130
## PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi 11130
## PRJNA830457_Sscl_periphery_BnapSentry_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi 11130
## PRJNA830457_Sscl_periphery_BoleTO1000_leaf_24hpi_vs_PRJNA830457_Sscl_periphery_BrapIMB218_leaf_24hpi 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
bpr <- "PRJNA874732"
this_meta <- meta %>% filter(bioproject == bpr)
#this_meta %>% select(included, sample, organism, accession, host, host_accession, tissue, hpi, plant_tissue, sample_material, notes, host_, organism_)
contrasts <- matrix(c(
'PRJNA874732_Sscl1980dSsAOX_colony_IVMMGluSSE', 'PRJNA874732_Sscl1980_colony_IVMMGluSSE', 'none'
), ncol=3, byrow = TRUE)
contrasts <- process_contrast_mat(bpr, contrasts, this_meta)
all_contrasts[[bpr]] <- contrasts
results <- run_deg_tests(lcounts, contrasts, bpr)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 284 rows containing non-finite values (`stat_bin()`).
## up
## PRJNA874732_Sscl1980dSsAOX_colony_IVMMGluSSE_vs_PRJNA874732_Sscl1980_colony_IVMMGluSSE 815
## down
## PRJNA874732_Sscl1980dSsAOX_colony_IVMMGluSSE_vs_PRJNA874732_Sscl1980_colony_IVMMGluSSE 1029
## zerocounts
## PRJNA874732_Sscl1980dSsAOX_colony_IVMMGluSSE_vs_PRJNA874732_Sscl1980_colony_IVMMGluSSE 284
## n
## PRJNA874732_Sscl1980dSsAOX_colony_IVMMGluSSE_vs_PRJNA874732_Sscl1980_colony_IVMMGluSSE 11130
norm_counts[[bpr]] <- results[["norm_counts"]]
deg_results <- c(deg_results, results[["tests"]])
Yay! They’re all done. Now I need to combine the results and turn it into something we can use for the next steps.
contrasts <- do.call(rbind, all_contrasts)
row.names(contrasts) <- NULL
deg_results_ <- do.call(rbind, lapply(
names(deg_results),
FUN = function(n) {
as.data.frame(deg_results[[n]]) %>% rownames_to_column("geneid") %>% mutate(contrast = n)
}
))
row.names(deg_results_) <- NULL
deg_results_ <- left_join(
deg_results_ %>% unique(),
contrasts %>% unique(),
by = "contrast"
) %>% unique()
## Warning in left_join(deg_results_ %>% unique(), contrasts %>% unique(), : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 701191 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.
deg_results_ <- deg_results_[
order(deg_results_$contrast_group, deg_results_$bioproject, deg_results_$contrast, deg_results_$geneid),
c('contrast', 'contrast_group', 'bioproject', 'sample1', 'sample2', 'host1', 'hpi1', 'host2', 'hpi2', 'geneid', 'baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue', 'padj')
]
readr::write_tsv(deg_results_, "output/dge_tests.tsv", na = "-")
head(deg_results_)
## contrast
## 2114701 PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi
## 2114702 PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi
## 2114703 PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi
## 2114704 PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi
## 2114705 PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi
## 2114706 PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi_vs_PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi
## contrast_group bioproject
## 2114701 AthavsBvulvul PRJNA670487
## 2114702 AthavsBvulvul PRJNA670487
## 2114703 AthavsBvulvul PRJNA670487
## 2114704 AthavsBvulvul PRJNA670487
## 2114705 AthavsBvulvul PRJNA670487
## 2114706 AthavsBvulvul PRJNA670487
## sample1
## 2114701 PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi
## 2114702 PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi
## 2114703 PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi
## 2114704 PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi
## 2114705 PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi
## 2114706 PRJNA670487_Sscl1980_center_AthaCol0_leaf_50hpi
## sample2 host1 hpi1 host2 hpi2
## 2114701 PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi Atha 50 Bvulvul 72
## 2114702 PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi Atha 50 Bvulvul 72
## 2114703 PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi Atha 50 Bvulvul 72
## 2114704 PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi Atha 50 Bvulvul 72
## 2114705 PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi Atha 50 Bvulvul 72
## 2114706 PRJNA670487_Sscl1980_center_Bvulvul_leaf_72hpi Atha 50 Bvulvul 72
## geneid baseMean log2FoldChange lfcSE stat
## 2114701 sscle_01g000010 672.4706 0.6758516 0.2289898 2.9514488
## 2114702 sscle_01g000020 698.0625 0.1922704 0.2487866 0.7728327
## 2114703 sscle_01g000030 324.3060 0.9304600 0.2955415 3.1483225
## 2114704 sscle_01g000040 707.8472 0.3738535 0.1703691 2.1943745
## 2114705 sscle_01g000050 2906.4103 1.7765442 0.5785149 3.0708700
## 2114706 sscle_01g000060 931.7485 -0.8675822 0.3706281 -2.3408428
## pvalue padj
## 2114701 0.003162870 0.014167290
## 2114702 0.439621409 0.615590480
## 2114703 0.001642104 0.008488059
## 2114704 0.028208494 0.077692936
## 2114705 0.002134360 0.010467397
## 2114706 0.019240268 0.057789684
Ok. thats the DGE tests done. Now the counts.
norm_counts_ <- do.call(cbind, norm_counts)
lcounts <- left_join(
lcounts,
as.data.frame(norm_counts_) %>% rownames_to_column("id") %>% tidyr::pivot_longer(-id, names_to = "sra", values_to = "disp_norm_count"),
by = c("sra", "id")
)
norm_counts_[1:5, 1:5]
## SRR1582088 SRR1582089 SRR1582090 SRR1582091 SRR1582133
## sscle_01g000010 6.369056 5.971994 6.087733 6.510656 6.460247
## sscle_01g000020 8.202125 8.449977 6.828782 7.757159 7.253096
## sscle_01g000030 6.046545 5.592120 5.395028 6.581483 5.882642
## sscle_01g000040 6.507843 6.917127 6.625821 5.996866 6.913650
## sscle_01g000050 6.544620 6.028905 9.393731 8.757588 9.573854
dim(norm_counts_)
## [1] 11130 422
That was easy. Let’s look again at the PCA plots.
pca_raw <- princomp(norm_counts_, fix_sign = TRUE)
prop_var <- ((pca_raw$sdev[1:10] / sum(pca_raw$sdev)) * 100) %>%
data.frame(variance_explained = .) %>%
rownames_to_column("PC") %>%
mutate(PC = paste0("PC", sprintf("%02d", 1:n())))
print(prop_var)
## PC variance_explained
## 1 PC01 23.901762
## 2 PC02 4.091779
## 3 PC03 2.742324
## 4 PC04 2.623500
## 5 PC05 2.230965
## 6 PC06 2.073388
## 7 PC07 1.778802
## 8 PC08 1.602252
## 9 PC09 1.538337
## 10 PC10 1.415452
ggplot(prop_var, aes(x=PC, y=variance_explained)) + geom_bar(stat = "identity")
pca <- as.data.frame(pca_raw$loadings[, 1:10])
colnames(pca) <- paste0("PC", sprintf("%02d", seq_len(ncol(pca))))
pca <- pca %>% rownames_to_column("sra")
pca <- right_join(meta, pca, by = "sra")
head(pca)
## # A tibble: 6 × 42
## sra bioproject biosample DOI platform read_length strategy stranded
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 SRR1582088 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 2 SRR1582089 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 3 SRR1582090 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 4 SRR1582091 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 5 SRR1582133 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## 6 SRR1582134 PRJNA261444 SAMN03074… http… ILLUMINA 150 PE unstran…
## # ℹ 34 more variables: n_fragments <dbl>, n_fragments_filtered <dbl>,
## # n_fragments_aligned <dbl>, n_fragments_aligned_single <dbl>,
## # n_fragments_aligned_multi <dbl>, included <chr>, sample <chr>,
## # organism <chr>, organism_tidyname <chr>, accession <chr>,
## # accession_tidyname <chr>, host <chr>, host_tidyname <chr>,
## # host_accession <chr>, host_accession_tidyname <chr>, tissue <chr>,
## # hpi <dbl>, plant_tissue <chr>, treatment <chr>, treatment_tidy <chr>, …
gg1 <- pca %>%
select(sra, host = host_tidyname, bioproject, PC01, PC02, PC03) %>%
tidyr::pivot_longer(cols = c(PC01, PC02, PC03)) %>%
full_join(., ., by=c("sra", "host", "bioproject")) %>%
unique() %>%
ggplot(aes(x=value.x, y=value.y, colour = host, shape = host)) +
geom_point(size = 2, alpha = 0.6) +
facet_grid(rows = vars(name.y), cols = vars(name.x), scales = "free") +
theme(aspect.ratio = 1)
## Warning in full_join(., ., by = c("sra", "host", "bioproject")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.
gg1 <- gg_shape(gg1, pca$host)
gg2 <- pca %>%
select(sra, host = host_tidyname, bioproject, PC01, PC02, PC03) %>%
tidyr::pivot_longer(cols = c(PC01, PC02, PC03)) %>%
full_join(., ., by=c("sra", "host", "bioproject")) %>%
unique() %>%
ggplot(aes(x=value.x, y=value.y, colour = bioproject, shape = bioproject)) +
geom_point(size = 2, alpha = 0.6) +
facet_grid(rows = vars(name.y), cols = vars(name.x), scales = "free") +
theme(aspect.ratio = 1)
## Warning in full_join(., ., by = c("sra", "host", "bioproject")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.
gg2 <- gg_shape(gg2, pca$bioproject)
library(cowplot)
##
## Attaching package: 'cowplot'
## The following object is masked from 'package:lubridate':
##
## stamp
gg <- plot_grid(gg1, gg2, ncol = 1, align = "hv")
ggsave("output/deseq_norm_pca.svg", gg, height = 10, width = 10)
gg
It’s a bit less crazy, which I like. I think there’s some nice clustering of hosts in there, so it seems like it should be possible to pull interesting things from this.
So in an ideal world, you’d have most of the variation explained by factors of interest. I do see a general clustering of samples from the same bioprojects. But there also tend to be similar treatments within each bioproject, so it’s difficult to tell.
We’ll have to see. Maybe I can remove some of these effects. I was planning to SVA to do this, but it seems like it can’t handle the DESeq2 counts shrunk counts. So i’ll run ComBat_seq on the raw data and redo the rlog shrinkage. Unfortunately, i can’t seem to include any of the covariates of interest in the ComBat model as they’re all confounded with the BioProject. So we’ll have to see if it removes any factors of interest.
batch <- setNames(meta[["bioproject"]], meta[["sra"]])[colnames(norm_counts_)]
tmp <- as.data.frame(column_to_rownames(meta, "sra")[colnames(norm_counts_), ])
tmp[is.na(tmp)] <- "NA"
tmp_counts <- lcounts %>% tidyr::pivot_wider(id_cols = c(id), names_from = sra, values_from = count) %>%
column_to_rownames("id") %>%
as.matrix()
mm <- model.matrix(~as.factor(sample), data=tmp)
# Paper says that applying shrinkage at this point is unnecessary.
combat_edata <- ComBat_seq(
tmp_counts,
batch=batch,
group=NULL,
shrink.disp = FALSE,
shrink = FALSE
)
## Found 30 batches
## Using null model in ComBat-seq.
## Adjusting for 0 covariate(s) or covariate level(s)
## Estimating dispersions
## Fitting the GLM model
## Shrinkage off - using GLM estimates for parameters
## Adjusting the data
combat_edata[1:5, 1:5]
## SRR1582088 SRR1582089 SRR1582090 SRR1582091 SRR1582133
## sscle_01g000010 35 45 59 57 184
## sscle_01g000020 72 167 65 82 177
## sscle_01g000030 17 18 15 46 65
## sscle_01g000040 32 74 78 44 176
## sscle_01g000050 50 73 628 290 1436
lcounts <- left_join(
lcounts,
as.data.frame(combat_edata) %>% rownames_to_column("id") %>% tidyr::pivot_longer(-id, names_to = "sra", values_to = "batch_norm_count"),
by = c("sra", "id")
)
head(lcounts)
## # A tibble: 6 × 25
## sra bioproject included sample organism organism_tidyname accession
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 SRR1582088 PRJNA261444 TRUE PRJNA261… Sclerot… Sscl Scl02-05
## 2 SRR1582088 PRJNA261444 TRUE PRJNA261… Sclerot… Sscl Scl02-05
## 3 SRR1582088 PRJNA261444 TRUE PRJNA261… Sclerot… Sscl Scl02-05
## 4 SRR1582088 PRJNA261444 TRUE PRJNA261… Sclerot… Sscl Scl02-05
## 5 SRR1582088 PRJNA261444 TRUE PRJNA261… Sclerot… Sscl Scl02-05
## 6 SRR1582088 PRJNA261444 TRUE PRJNA261… Sclerot… Sscl Scl02-05
## # ℹ 18 more variables: accession_tidyname <chr>, host <chr>,
## # host_tidyname <chr>, host_accession <chr>, host_accession_tidyname <chr>,
## # tissue <chr>, hpi <dbl>, plant_tissue <chr>, treatment <chr>,
## # treatment_tidy <chr>, sample_material <chr>, host_ <chr>, organism_ <chr>,
## # id <chr>, count <dbl>, cpm <dbl>, disp_norm_count <dbl>,
## # batch_norm_count <dbl>
Ok. So we’ve done the batch correction, now we do the DESeq correction again.
run_count_norms2 <- function(lcounts, bpr) {
this_counts <- lcounts %>% filter(bioproject == bpr) %>% tidyr::pivot_wider(id_cols = c(id), names_from = sra, values_from = batch_norm_count) %>%
column_to_rownames("id") %>%
as.matrix()
all(rownames(this_meta) == colnames(this_counts))
this_meta_ <- this_meta %>% column_to_rownames("sra") %>% mutate(sample = factor(sample)) %>% .[colnames(this_counts), ]
dds <- DESeqDataSetFromMatrix(
countData = this_counts,
colData = this_meta_,
design = ~ 1
)
ncounts <- assay(rlog(dds, blind = TRUE))
print(plotPCA(rlog(dds, blind = FALSE), intgroup = "sample"))
return(ncounts)
}
norm_counts <- list()
for (bpr in unique(meta$bioproject)) {
this_meta <- meta %>% filter(bioproject == bpr)
norm_counts[[bpr]] <- run_count_norms2(lcounts, bpr)
}
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## rlog() may take a few minutes with 30 or more samples,
## vst() is a much faster transformation
## rlog() may take a few minutes with 30 or more samples,
## vst() is a much faster transformation
## converting counts to integer mode
## rlog() may take a few minutes with 30 or more samples,
## vst() is a much faster transformation
## rlog() may take a few minutes with 30 or more samples,
## vst() is a much faster transformation
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## converting counts to integer mode
## rlog() may take a long time with 50 or more samples,
## vst() is a much faster transformation
## rlog() may take a long time with 50 or more samples,
## vst() is a much faster transformation
## converting counts to integer mode
## converting counts to integer mode
norm_counts_ <- do.call(cbind, norm_counts)
lcounts <- left_join(
lcounts,
as.data.frame(norm_counts_) %>% rownames_to_column("id") %>% tidyr::pivot_longer(-id, names_to = "sra", values_to = "norm_count"),
by = c("sra", "id")
)
norm_counts_[1:5, 1:5]
## SRR1582088 SRR1582089 SRR1582090 SRR1582091 SRR1582133
## sscle_01g000010 7.072656 6.711151 6.802304 7.135453 7.188496
## sscle_01g000020 7.913726 8.082829 7.092964 7.646709 7.331486
## sscle_01g000030 5.978562 5.480902 5.224116 6.514275 5.810956
## sscle_01g000040 7.038773 7.194144 7.093917 6.949972 7.188252
## sscle_01g000050 7.718862 7.467015 9.415718 9.025624 9.543757
dim(norm_counts_)
## [1] 11130 422
The per bioproject PCA plots look very similar to the others, though it’s difficult to compare them scrolling up and down. Now let’s look at the whole PCA.
tmp_counts <- apply(norm_counts_, MARGIN = 2, FUN = scale)
rownames(tmp_counts) <- rownames(norm_counts_)
colnames(tmp_counts) <- colnames(norm_counts_)
tmp_counts[is.na(tmp_counts)] <- 0
pca_raw <- princomp(tmp_counts, fix_sign = TRUE)
prop_var <- ((pca_raw$sdev[1:10] / sum(pca_raw$sdev)) * 100) %>%
data.frame(variance_explained = .) %>%
rownames_to_column("PC") %>%
mutate(PC = paste0("PC", sprintf("%02d", 1:n())))
print(prop_var)
## PC variance_explained
## 1 PC01 27.825656
## 2 PC02 3.130342
## 3 PC03 2.820906
## 4 PC04 2.095826
## 5 PC05 1.723563
## 6 PC06 1.442321
## 7 PC07 1.334999
## 8 PC08 1.271084
## 9 PC09 1.156804
## 10 PC10 1.122988
ggplot(prop_var, aes(x=PC, y=variance_explained)) + geom_bar(stat = "identity")
We’ve jumped from the first PC explaining ~23% of the variance to ~27%. It’s not much of a difference.
pca <- as.data.frame(pca_raw$loadings[, 1:10])
colnames(pca) <- paste0("PC", sprintf("%02d", seq_len(ncol(pca))))
pca <- pca %>% rownames_to_column("sra")
pca <- right_join(meta %>% select(sra, host_tidyname, bioproject), pca, by = "sra")
gg1 <- pca %>%
select(sra, host = host_tidyname, bioproject, PC01, PC02, PC03) %>%
tidyr::pivot_longer(cols = c(PC01, PC02, PC03)) %>%
full_join(., ., by=c("sra", "host", "bioproject")) %>%
unique() %>%
ggplot(aes(x=value.x, y=value.y, colour = host, shape = host)) +
geom_point(size = 2, alpha = 0.6) +
facet_grid(rows = vars(name.y), cols = vars(name.x), scales = "free") +
theme(aspect.ratio = 1)
## Warning in full_join(., ., by = c("sra", "host", "bioproject")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.
gg1 <- gg_shape(gg1, pca$host)
gg2 <- pca %>%
select(sra, host = host_tidyname, bioproject, PC01, PC02, PC03) %>%
tidyr::pivot_longer(cols = c(PC01, PC02, PC03)) %>%
full_join(., ., by=c("sra", "host", "bioproject")) %>%
unique() %>%
ggplot(aes(x=value.x, y=value.y, colour = bioproject, shape = bioproject)) +
geom_point(size = 2, alpha = 0.6) +
facet_grid(rows = vars(name.y), cols = vars(name.x), scales = "free") +
theme(aspect.ratio = 1)
## Warning in full_join(., ., by = c("sra", "host", "bioproject")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.
gg2 <- gg_shape(gg2, pca$bioproject)
library(cowplot)
gg <- plot_grid(gg1, gg2, ncol = 1, align = "hv")
ggsave("output/combat_norm_pca.svg", gg, height = 10, width = 10)
gg
gg3 <- pca %>%
select(sra, host = host_tidyname, bioproject, PC01, PC02, PC03) %>%
tidyr::pivot_longer(cols = c(PC01, PC02, PC03)) %>%
unique() %>%
ggplot(aes(x=value)) +
geom_histogram(fill = "black") +
facet_grid(cols = vars(name), scales = "free")
print(gg3)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Yeah i do think that the PCs are maybe a bit tidier. I definitely still see groupings of samples from the same bioproject, but probably it’s unavoidable. Maybe i’ll look at correlation matrices.
Here’s the one from the ComBat normalised counts.
tmp <- lcounts %>% tidyr::pivot_wider(id_cols = c(id), names_from = sra, values_from = norm_count) %>%
column_to_rownames("id") %>%
as.matrix()
meta_tmp <- meta[order(meta$host, meta$host_accession, meta$hpi, meta$tissue, meta$accession, meta$plant_tissue, meta$treatment),]
tmp <- tmp[, meta_tmp$sra[meta_tmp$sra %in% colnames(tmp)]]
tmp_cor <- cor(tmp)
#tmp_dist <- as.dist(1 - tmp_cor)
#hcl <- hclust(tmp_dist, method = "average")
#srrs <- colnames(tmp)[hcl$order]
ha <- HeatmapAnnotation(
bioproject = meta %>% column_to_rownames("sra") %>% .[colnames(tmp), "bioproject"],
host = meta %>% column_to_rownames("sra") %>% .[colnames(tmp), "host"]
)
ht <- Heatmap(
tmp_cor,
name = "samples",
cluster_rows = FALSE,
cluster_columns = FALSE,
show_column_dend = FALSE,
show_column_names = FALSE,
show_row_names = FALSE,
top_annotation = ha,
height=nrow(tmp_cor) * unit(0.5, "mm"),
width=ncol(tmp_cor) * unit(0.5, "mm")
)
htsize <- calc_ht_size(ht)
htsize
## [1] 12.409344 8.906201
pdf("output/06-samples_correlation_host_normed.pdf", width = htsize[1], height = htsize[2])
ht
dev.off()
## png
## 2
draw(ht)
Well the clusters in here certainly make more sense than from the raw
data. I’ve looked at the data, and i don’t think a log transformation is
necessary.
And here’s the one from the dispersion normalised counts (i.e. DESeq but no ComBat).
tmp <- lcounts %>% tidyr::pivot_wider(id_cols = c(id), names_from = sra, values_from = disp_norm_count) %>%
column_to_rownames("id") %>%
as.matrix()
meta_tmp <- meta[order(meta$host, meta$host_accession, meta$hpi, meta$tissue, meta$accession, meta$plant_tissue, meta$treatment),]
tmp <- tmp[, meta_tmp$sra[meta_tmp$sra %in% colnames(tmp)]]
tmp_cor <- cor(tmp)
#tmp_dist <- as.dist(1 - tmp_cor)
#hcl <- hclust(tmp_dist, method = "average")
#srrs <- colnames(tmp)[hcl$order]
ha <- HeatmapAnnotation(
bioproject = meta %>% column_to_rownames("sra") %>% .[colnames(tmp), "bioproject"],
host = meta %>% column_to_rownames("sra") %>% .[colnames(tmp), "host"]
)
ht <- Heatmap(
tmp_cor,
name = "samples",
cluster_rows = FALSE,
cluster_columns = FALSE,
show_column_dend = FALSE,
show_column_names = FALSE,
show_row_names = FALSE,
top_annotation = ha,
height=nrow(tmp_cor) * unit(0.5, "mm"),
width=ncol(tmp_cor) * unit(0.5, "mm")
)
htsize <- calc_ht_size(ht)
htsize
## [1] 12.409344 8.906201
pdf("output/06-samples_correlation_host_normed.pdf", width = htsize[1], height = htsize[2])
ht
dev.off()
## png
## 2
draw(ht)
These look almost identical. I think that because the extra amount of variance explained is relatively low, and the results look fairly similar, i won’t continue with the ComBat normalised counts. There’s a risk that removing the bioproject effect might remove some information about treatments (esp because I wasn’t able to provide info about treatments). The benefits don’t seem to outweigh the risks.
I’d like to see what we can do with gene level correlation. Do the gene expression values look ok?
tmp <- lcounts %>% tidyr::pivot_wider(id_cols = c(id), names_from = sra, values_from = disp_norm_count) %>%
column_to_rownames("id") %>%
as.matrix()
meta_tmp <- meta[order(meta$host, meta$host_accession, meta$hpi, meta$tissue, meta$accession, meta$plant_tissue, meta$treatment),]
tmp <- tmp[, meta_tmp$sra[meta_tmp$sra %in% colnames(tmp)]]
# There are two genes with no counts in any samples.
# We need to exclude them for the correlation to work.
# Causes error about stddev == 0.
tmp_cor_disp <- cor(t(tmp[(rowSums(tmp) > 0), ]))
quantile(tmp_cor_disp)
## 0% 25% 50% 75% 100%
## -0.7794173 0.5212797 0.7029208 0.8299517 1.0000000
Yeah that seems like a reasonable range of values. I think probably the number of genes with mid correlation (~0.5-0.8) is caused by genes with relatively low variation across treatments. I suspect that once we remove those genes we’ll see cleaner differences.
pca_raw <- prcomp(t(tmp), retx = TRUE)
prop_var <- ((pca_raw$sdev[1:10] / sum(pca_raw$sdev)) * 100) %>%
data.frame(variance_explained = .) %>%
rownames_to_column("PC") %>%
mutate(PC = paste0("PC", sprintf("%02d", 1:n())))
print(prop_var)
## PC variance_explained
## 1 PC01 12.060582
## 2 PC02 5.330749
## 3 PC03 3.133397
## 4 PC04 3.026981
## 5 PC05 2.468580
## 6 PC06 2.090405
## 7 PC07 1.965078
## 8 PC08 1.874302
## 9 PC09 1.683586
## 10 PC10 1.516939
ggplot(prop_var, aes(x=PC, y=variance_explained)) + geom_bar(stat = "identity")
pca <- as.data.frame(pca_raw$rotation[, 1:10])
colnames(pca) <- paste0("PC", sprintf("%02d", seq_len(ncol(pca))))
pca <- pca %>% rownames_to_column("id")
gg <- ggplot(pca, aes(x=PC01, y=PC02)) +
geom_point(alpha = 0.7, size = 3)
print(gg)
gg <- ggplot(pca, aes(x=PC03, y=PC04)) +
geom_point(alpha = 0.7, size = 3)
gg
I guess the values look fine. It’s a bit hard to say but there aren’t like crazy clusters or massive outliers.
We’ll continue with the dispersion corrected counts. I just want to check though whether there are mean differences left in the data.
ggplot(lcounts, aes(x = sra, y = disp_norm_count, colour = bioproject)) + geom_boxplot() + facet_wrap(vars(bioproject), scales = "free_x")
I do see differences in mean and scale between bioprojects. They’re pretty similar in terms of the range, but it might be a problem later if we do anything other than correlation clustering. How are the ComBat counts?
ggplot(lcounts, aes(x = sra, y = norm_count, colour = bioproject)) + geom_boxplot() + facet_wrap(vars(bioproject), scales = "free_x")
I seems like the main contribution here is that some of the crazy outliers in PRJNA670487 are squished. Otherwise the counts have pretty similar ranges.
I think maybe i’m coming back around to ComBat just for the sake of shrinking the outliers?
I’m just going to save them both and we can decide later :)
lcounts %>%
tidyr::pivot_wider(id_cols = id, names_from = sra, values_from = disp_norm_count) %>%
readr::write_tsv("output/feature_counts_deseq2_normed.tsv", na = "-")
lcounts %>%
tidyr::pivot_wider(id_cols = id, names_from = sra, values_from = norm_count) %>%
readr::write_tsv("output/feature_counts_combat_normed.tsv", na = "-")
BYEEE!